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In order to improve the stock price prediction effect, based on the improved PSO algorithm, this paper constructs a stock price prediction model through neural network. Based on the idea of avoiding particles falling into the same local solution as much as possible and always keeping the particles with a certain diversity, in order to improve the global search ability of the algorithm in the early stage of evolution and the local search ability in the later stage of the evolution, the adaptive adjustment of inertial weight is proposed, and the algorithm is improved by combining with neural network. In addition, on the basis of the improved algorithm, this paper constructs a stock price prediction system based on neural network. Finally, this paper designs experiments to verify the function of the model from the perspectives of stock data collection and processing, and stock price prediction accuracy, and draw statistical graphs based on the statistical research results. The results of the research show that the system constructed in this paper has a certain practical effect. [ABSTRACT FROM AUTHOR]

This paper recommends developing advanced backtracking search (ABS) to use single- and multi-vector mutation strategies to effectively enhance the backtracking search algorithm in solving a variety of optimization problems. The ABS version proposed in this paper utilizes three primary strategies considering critical historical information and suitable crossover mechanisms. Two of these are single-vector strategies that conduct searches based on random individuals, respectively, heading toward historical positions and determining destinations with one perturbation vector. The remaining multi-vector strategy conducts searches around historical best positions using a relatively low crossover rate. The performance of the suggested six ABS versions was evaluated using the benchmark functions of IEEE CEC2005 and CEC2019. The experimental results demonstrate that the proposed ABS version significantly improves BSA and its improved version. Additionally, the proposed ABS version is the most competitive algorithm compared to the seven classical algorithms in terms of evaluations of obtained results and significant values. [ABSTRACT FROM AUTHOR]

With the development of formal concept analysis, classical concept lattice cannot solve some problems in practice because of its strict conditions. Protoconcept, which is an extension of the formal concept, provides a new method of data processing. Three-way decisions is more in line with human cognition. Not like the decision of two ways, three-way cognition expands its scope of application. However, up to now, protoconcept is influenced by two-way decisions which leads to the restrictions of its applications. Hence, it is urgent to find a method to generalize protoconcept from the framework of two-way decisions. To give the solution to this urgent problem, the main contributions of this paper are the following: First, using the combination of protoconcept and three-way decisions, two models of three-way protoconcept are obtained. The algorithms for searching three-way protoconcept are built. Second, we investigate the constructions of three-way protoconcepts in double Boolean algebra. Third, the relationships between protoconcepts and three-way protoconcepts are found. Moreover, using examples that come from the real world, we explain the main results obtained above and show some applications. The two models for protoconcepts provided in this paper generalize the fields of thought from two-way decisions to three-way decisions. This follows that they are more in line with human thinking. Hence, the two models will certainly generalize the applications of protoconcept theory in the future. [ABSTRACT FROM AUTHOR]

Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit parallelism in population-based search algorithms, to solve multiple coupled subsystems simultaneously in a complex system. The proposed MSGA is composed of within-subsystem evolution and cross-subsystem migration operators. The objective of the former is to optimize each subsystem by appropriate search strategies, and the objective of the latter is to exchange information between multiple subsystems by migration, which is based on the similarity probability of objectives and constraints, and the intersection probability of solutions in different subsystems. During migration across subsystems, three statistical approaches of measuring similarity and three metrics of solution intersection in information theory are used to calculate these probabilities. Performance is tested on a set of multi-subsystem benchmark functions, and the simulation results show that cross-subsystem migration plays the key role for the performance of MSGA. Furthermore, the proposed MSGA is compared with other competitive algorithms, and results show that it is a promising multi-system optimization algorithm. In summary, the contribution of this paper is the introduction of multi-system optimization to the EA community. [ABSTRACT FROM AUTHOR]

This paper considers a special vehicle routing problem, the two-echelon vehicle routing problem with simultaneous delivery and pickup demands (2E-VRPSDP). The 2EVRPSDP differs from classic transportation and vehicle routing problems in two ways. First, freight delivery from the depot to the customers is managed by shipping the freight through intermediate satellites. Second, each customer in the 2E-VRPSDP may have simultaneous delivery and pickup demands. The 2E-VRPSDP is an extension of the two-echelon vehicle routing problem (2E-VRP) and the vehicle routing problem with simultaneous delivery and pickup (VRPSDP). A variable neighborhood search algorithm is designed to solve the 2E-VRPSDP in which both feasible and infeasible solutions can be explored. Numerical results show that the proposed algorithm is effective and that the algorithm can provide reasonable solutions within an acceptable computational time. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of existing algorithms in solving mixed-integer programming problems, such as local convergence and poor solution accuracy, this paper presents a hybrid improved sine and cosine algorithm. The improved position update formula is introduced to enhance the global and local search abilities of the algorithm, and combined mutation is further given to avoid local convergence. The performance of proposed algorithm is validated by the high-dimensional modified CEC 2017 COPs and two complex engineering optimization problems. The experimental findings indicate that the hybrid mechanism of improved sine and cosine algorithm and combined mutation is demonstrably effective, and the proposed algorithm provides a satisfactory solution for addressing high-dimensional complicated mixed-integer programming problems. [ABSTRACT FROM AUTHOR]

Latest advancements in real-time remote sensing sensors and platform lead to the development of unmanned aerial vehicles (UAV) which enable the accessibility of high-resolution imaging data. Since image classification appears like a basic interconnection among aerial images and the corresponding applications, it intends to categorize images into semantic classes. Several earlier works have concentrated on the classification of an image into a single semantic label, whereas in the real world, an aerial image is commonly interrelated to many class labels, for instance, multiple object-level labels. Moreover, a broad image of present objects in provided high-resolution aerial image offers an extensive interpretation of the investigated area. To attain this, this paper presents a new quantum neural network based multilabeled aerial image classification (QNN-MLAIC) model. The proposed QNN-MLAIC model involves different processes, namely image acquisition, preprocessing, object detection, feature extraction, and classification. Initially, the aerial images are acquired by wireless UAVs and are preprocessed. Then, the Faster RCNN technique with Inception with Residual Network-v2 model as the baseline model is applied as an object detector, which detects the existence of multiple objects in the aerial image and generates a helpful set of feature vectors. Finally, QNN is employed as classifier, which categorizes the aerial images into multiple class labels. In order to tune the parameters involved in QNN model, the beetle antenna search algorithm is employed. Detailed performance analysis of the proposed QNN-MLAIC model takes place using the UC Merced dataset (UCM) aerial dataset, and the outcomes are investigated under many dimensions. The experimental outcome ensured the goodness of the presented QNN-MLAIC method on the applied UCM aerial dataset over the compared methods. [ABSTRACT FROM AUTHOR]

Cooperation search algorithm (CSA) is a new metaheuristic algorithm inspired from the team cooperation behaviors in modern enterprises and is characterized by fast convergence. However, for complex multimodal problems, it may get trapped into local optima and suffer from premature convergence for the shortcoming of population updating guided only by leading individuals. In this paper, the issue of low convergence efficiency and convergence accuracy of the CSA algorithm on complex multimodal problems is dramatically alleviated by integrating the mutation and crossover operators in DE algorithm. Experimental results demonstrate the better performance of CCSA on convergence speed and accuracy as compared to other existing optimizers. Furthermore, aiming at the problem that there is no universal approach for the multi-degree reduction in Ball Bézier surfaces under different interpolation constrains, we propose a new method to solve this problem by introducing metaheuristic methods, where the change of interpolation constrains is treated as the change of decision variables. The modeling examples show that the proposed method is effective and easy to implement under different interpolation constrains, which can achieve the multi-degree reduction in Ball Bézier surfaces at one time and can simplify the degree reduction procedure significantly. [ABSTRACT FROM AUTHOR]

In this paper Enhanced Heat Transfer Search algorithm and Enriched Replicated Coronary Circulation System Optimization algorithm are applied to solve the real power loss reduction problem. Key objectives of the paper are Voltage stability enhancement, Voltage deviation minimization and real power loss reduction. Heat transfer algorithm has been designed on the basis of law of thermodynamic state. It is regular procedure that any organism will try to attain to reach the state of equilibrium with respect to the environments. Heat transfer will occur by conduction, convection and radiation. Through arbitrarily engendered population, Heat transfer algorithm is designed with molecules and the temperature conditions. Population is modernized in every generation through arbitrarily designated heat transmission methods. In the enhanced heat transfer search algorithm reinforcement of population has been intermingled with the algorithm. Synchronized heat transfer and population reinforcement are amalgamated into the Heat Transfer Search algorithm to augment the quickness in the exploration procedure, to progress the convergence degree, and to evade local optimum. Replicated Coronary Circulation System Optimization Algorithm has been designed by emulating the activities of human heart veins (coronary artery progress). In the proposed algorithm contender solution is formulated by considering capillaries actions. Subsequently the Coronary progress factor evaluates the solution and population the area has been originated capriciously. In the Enhanced Replicated Coronary Circulation System Optimization Algorithm the Heart memory will stock pile the finest solutions and modernize them sequentially. The HMYempowers to have a sturdier exploitation and improved convergence. Additionally, method applied to modify some modules of capillaries frontrunners which helps the procedure to spurt from local optima solution. Proposed Enhanced Heat Transfer Search algorithm and Enriched Replicated Coronary Circulation System Optimization algorithm are appraised in IEEE 30 bus system and IEEE 14, 30, 57, 118, 300 bus test systems without considering the voltage constancy index. True power loss lessening, voltage divergence curtailing, and voltage constancy index augmentation have been attained. [ABSTRACT FROM AUTHOR]

In this paper, Belleville Washer Search optimization algorithm, Papilionoidea Optimization Algorithm, Opposition-based Learning integrated with Papilionoidea Optimization Algorithm, Chaotic Local Search integrated with Papilionoidea Optimization Algorithm and then both the Opposition-based Learning and Chaotic Local Search combined with Papilionoidea Optimization Algorithm are designed to solve the optimization problem. Key objectives of the paper are the minimization of voltage deviation, voltage stability enhancement and power loss reduction. Belleville washer is coned-disc spring, which can encumber the length of its axis by static and dynamic mode. Belleville washers are helpful for modification since dissimilar breadth can be exchanged within and away and it can be used to attain vast tune of coil rate. They are perfect in circumstances wherever a weighty coil power is necessary with least complimentary distance end to end and firmness previous to attainment of firm elevation. Fundamental law of force has been employed to design the Belleville Washer Search optimization algorithm. Belleville washer impresses a force on the particle in horizontal direction and an external force pressure the particle in anti-clockwise direction. Belleville washer's force is equal to the peripheral force in the period of time the particle will be in balanced condition. Initially, the particle moves certain distance, and Belleville washer's force will tender a resistance over the particle during the time peripheral force acting over the particle. Stiffness property of the Belleville washer is tailored relative to the objective function. Force and movement of the particle have been observed. Papilionoidea Optimization Algorithm is based on performance of the Papilionoidea in nutrition foraging. Opposition-based Learning is a technique integrated with Papilionoidea Optimization Algorithm. Through this, exploration capability of the process will be enriched and convergence rate will be augmented. Then, in this work, Chaotic Local Search integrated with Papilionoidea Optimization Algorithm. Through this integration, the algorithm will balance the exploration and exploitation equally. Then, both the Opposition-based Learning and Chaotic Local Search has been integrated with Papilionoidea Optimization Algorithm. With and without voltage stability, index Belleville Washer Search optimization algorithm, Papilionoidea Optimization Algorithm, Opposition-based Learning integrated with Papilionoidea Optimization Algorithm, Chaotic Local Search integrated with Papilionoidea Optimization Algorithm and then both the Opposition-based Learning and Chaotic Local Search combined with Papilionoidea Optimization Algorithm are corroborated in standard IEEE 30-bus test systems. Proposed algorithms condensed the power loss competently with augmentation in voltage stability and minimization of voltage deviation. [ABSTRACT FROM AUTHOR]

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA's performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA's significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. [ABSTRACT FROM AUTHOR]

This paper proposes a jellyfish search algorithm (JSA)-based offline method to design a DC voltage controller of AC/DC front-end converter (FEC) in a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). First, a multi-objective tuning problem is formulated using a full-order converter model to achieve maximum utilization of converter parameters in controller design. Next, a normalized mono-objective problem is developed using a utopia-tracking approach to reduce the computational complexity in design. Finally, the mono-objective problem is solved using JSA to enhance the controller performance by obtaining optimal gain values for the controller. A comparative study is performed with other metaheuristic optimization techniques to justify the use of JSA in this application. The results confirm that the accuracy of the JSA-based approach is higher, whereas the execution time of the method is lower than the other methods. Furthermore, to validate the performance of the proposed approach, a DFIG-based WECS is modeled and simulated using OPAL-RT real-time simulator. A grid voltage-oriented vector control is used as the overall control algorithm for the FEC. Unlike the conventional pole-zero cancellation method, the proposed approach provides no voltage overshoot during voltage development. Compared to the published literature, it also ensures 40.65% lesser voltage deviation without any overcurrent, irrespective of wind speed and load variations. [ABSTRACT FROM AUTHOR]

Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in turn is combined with purely stochastic operators. According to the obtained outcomes, opt-IA shows strictly better performances than almost all heuristics and metaheuristics to which it was compared; whilst it turns out to be comparable with the Hyper-Heuristic method. Overall, it can be claimed that opt-IA, even if driven by a purely random process, proves to be reliable and with efficient performance. Furthermore, to prove the latter claim, a sensitivity analysis of the functionality was conducted, using the classic metrics NMI, ARI and NVI. [ABSTRACT FROM AUTHOR]

The primary objective of airport management worldwide is always to make it easier to provide transportation services and minimize latency. This could be accomplished by controlling the movement of travelers through the airport's different checkpoints for passports, baggage handling, customs, and both departure and arrival lobbies. As one of the biggest passenger terminals around the world and among the most attractive destinations for visitors during the Hajj pilgrimage, this paper concentrates on enhancing the movement of travelers in the King Abdulaziz International Airport's pilgrimage station in the Kingdom of Saudi Arabia. Several optimization methods are used to better schedule the phases within the airport terminals as well as the assignment of arriving flights to vacant airport portals. These include the differential evolution algorithm (DEA), harmony search algorithm, genetic algorithm (GA), flower pollination algorithm (FPA), and black widow optimization algorithm. The findings demonstrated the potential sites for the development of airport stages, which may assist decision-makers in improving operational efficiency in the future. The simulation results showed that GA was more efficient in most of the experiments than the alternative algorithms for small population sizes in terms of the quality of the solutions obtained and the convergence rates. In contrast, DEA performed better in the larger population sizes. The outcomes also showed that FPA performed better than its rivals in identifying the optimal solution in terms of the overall duration of passenger waiting time. [ABSTRACT FROM AUTHOR]

Convergence analysis of any random search algorithm helps to verify whether and how quickly the algorithm guarantees convergence to the point of interest. Butterfly optimization algorithm (BOA) is a popular population-based stochastic optimizer introduced by mimicking the foraging behaviors of butterflies in nature. In this paper, we have developed the Markov chain model of the BOA and analyzed the convergence behavior of the algorithm. The Markov chain model of the BOA is constituted where the population sequence generated by the algorithm is found to be a finite homogenous Markov chain and the defined population state set is found to be reducible. Convergence analysis of the algorithm is performed mathematically using the Markov chain model of the algorithm with the help of global convergence theorem which is based on a random search algorithm satisfying two subtle conditions. The butterfly algorithm has been found to satisfy the conditions for the global convergence theorem to enact, whereof it guarantees the global convergence of the BOA. We have also tried to show experimentally that the convergence of the algorithm does not always have considerable impact on rate of convergence as it is influenced by various other factors. Moreover, the convergence of BOA has been compared with several state-of-the-art algorithms experimentally. Further, the effects of the parameters, namely sensory modality and power exponent on the performance of BOA, have been studied. [ABSTRACT FROM AUTHOR]

Lindenmayer systems (L-systems) are a grammar system that consists of string rewriting rules. The rules replace every symbol in a string in parallel with a successor to produce the next string, and this procedure iterates. In a stochastic context-free L-system (S0L-system), every symbol may have one or more rewriting rule, each with an associated probability of selection. Properly constructed rewriting rules have been found to be useful for modeling and simulating some natural and human engineered processes where each derived string describes a step in the simulation. Typically, processes are modeled by experts who meticulously construct the rules based on measurements or domain knowledge of the process. This paper presents an automated approach to finding stochastic L-systems, given a set of string sequences as input. The implemented tool is called the Plant Model Inference Tool for S0L-systems or PMIT-S0L. PMIT-S0L is evaluated using 960 procedurally generated S0L-systems in a test suite, which are each used to generate input strings, and PMIT-S0L is then used to infer the system from only the sequences. The evaluation shows that PMIT-S0L infers S0L-systems with up to 9 rewriting rules each in under 12 hours. Additionally, it is found that 3 sequences of strings are sufficient to find the correct original rewriting rules in 100 % of the cases in the test suite, and 6 sequences of strings reduce the difference in the associated probabilities to approximately 1 % or less. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of the traditional parameter identification methods of photovoltaic cell model, including low accuracy, slow convergence speed, easy to be trapped in local optimum so on, a parameter identification method of photovoltaic cell model based on improved elephant herding optimization algorithm is proposed in this paper. The fast-moving operator is used, which greatly improves the convergence speed and global searching ability of the algorithm. The elitist strategy is also introduced where the worst individual is replaced with the optimal individual to improve the convergence speed and shorten the optimization time of the algorithm. When applied to the parameter identification of the photovoltaic cell model, the identification result of the improved elephant herding optimization algorithm proves to be better than other algorithms. The parameter identification of the photovoltaic cell model under different radiation conditions is carried out, and the fitting result of identification data is highly consistent with the measured data. Experiments validate that the improved elephant herding optimization algorithm can accurately and effectively identify the parameters of the photovoltaic cell model in different environments. [ABSTRACT FROM AUTHOR]

Binary sequences with low aperiodic autocorrelation play an important role in communication and radar. The search of long low autocorrelation binary sequences (LABS) is a classical but challenging research problem. Exhaustive sequence search is infeasible for large lengths due to its prohibitively high complexity. Albeit many algorithms on LABS search in the literature, they are effective for lengths up to a few hundred only. In this paper, we propose an efficient hybrid algorithm which combines the strength of coordinate descent algorithm and the simulated annealing algorithm. Our proposed hybrid algorithm can efficiently generate long LABS of lengths up to several thousand and outperforms existing approaches in most cases. [ABSTRACT FROM AUTHOR]

In PV power generation systems, flexible power point tracking control needs to be introduced to regulate the output power on the PV side for meeting the grid connection requirements. However, traditional FPPT control methods tend to cause certain oscillations in the system. This paper proposes an FPPT method based on the adaptive lion swarm optimization algorithm (ALSO). First, an ALSO algorithm is proposed, which adds adaptive scaling parameters to the lion swarm optimization algorithm, so that the number of adult lions can be adjusted adaptively with increasing iterations to coordinate the local exploitation and global search abilities of the algorithm. Then the ALSO is used to selectively optimize the operation of different intervals so as to achieve the FPPT control on the left or right side of the maximum power point. The test experiments for the ALSO algorithm show that the ALSO algorithm has strong local exploitation and global search abilities, which is better than the compared algorithms. Through the simulations on the PV grid-connected system, the validity and superiority of the ALSO-based method over the traditional FPPT algorithm in terms of dynamic and steady-state performance are verified under different operating conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a simple and effective optimization algorithm named as "Improved Rao (I-Rao) algorithm". The I-Rao algorithm consists of two phases: the local exploitation phase and the global exploration phase. The local exploitation phase improves the exploitation of the search process of Rao algorithms and enhances the algorithm's convergence speed. The global exploration phase helps the algorithm to get away from the local optimum solutions and enhances the exploration ability of the search process of the algorithm. Like Rao algorithms, the I-Rao algorithm also does not need algorithm-specific control parameters. The effectiveness of the I-Rao algorithm is tested on 25 unconstrained benchmark functions, 45 complex CEC test functions, and 19 real-world constrained mechanical design optimization problems of CEC2020. The comparison of results reveals the effectiveness of the I-Rao algorithm over many state-of-the-art advanced optimization algorithms such as GWO, SCA, PSO, WOA, Jaya, IJAYA, MJAYA, PGJAYA, EJAYA, IUDE, εMAgES, iLSHADEε, and COLSHADE. The Friedman test is also carried out to check the overall performance of the I-Rao algorithm as compared to other advanced optimization algorithms considered. The convergence plots illustrate the convergence speed of the I-Rao algorithm. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs. [ABSTRACT FROM AUTHOR]

Because of the complexity of data sets from the real world, it is difficult to classify the data sets clearly and effectively, thus we prefer to adopt fuzzy clustering approaches to analyze the data sets. However, due to the variety of fuzzy clustering algorithms, the different number of clusters will lead to different clustering results. The number of clusters is closely related to the clustering division, so how to determine the number of fuzzy clustering (k) has become a problem. Until now, many researchers have proposed utilizing fuzzy clustering validity indexes to deal with this kind of problem. However, the effectiveness index of fuzzy clustering can only be evaluated on the basis of the fuzzy clustering algorithm FCM to divide the clusters. When the range of k value is too large, FCM's clustering for different k values is quite time-consuming. From this perspective, this paper proposes a fuzzy clustering optimal k selection method based on multi-objective optimization (FMOEA-K). Different from the traditional methods, this method combines the fuzzy clustering effectiveness index with multi-objective optimization algorithm (MOEA), and uses multi-objective optimization algorithm to search the appropriate cluster center concurrently. Because of the concurrency of the multi-objective optimization algorithm, the calculation time is shortened. The experimental results show that compared with the traditional method, the FMOEA-K can shorten the calculation time and improve the accuracy of calculating the optimal k value. [ABSTRACT FROM AUTHOR]

University course timetabling is a NP-hard problem that be performed for each semester frequently. In this paper, we use a two-step algorithm for timetabling of common lecturers among departments. In the first step, we use a fuzzy multi-criteria decision-making comparison and local search algorithms with seven neighborhood structures and random iteration. It means that we use a fuzzy multi-criteria comparison algorithm to eliminate the ambiguities and soft constraints of common lecturers among departments. In addition, we apply the local search algorithm with seven neighboring structures to avoid trapping into local optima and improve the fuzzy multi-criteria comparison over the preferences and soft constraints of lecturers. In the second step, the common lecturers' timetable generated in the first step by the clustering approach (k-means, fuzzy c-means and funnel shape) is clustered based on the preferences and soft constraints of common lecturers among departments. Now, our common lecturers prepared by the clustering algorithms are mapped to the traversed free resources according to the paper's aims: (1) descending satisfaction of preferences and soft constraints of common lecturers among departments and (2) minimizing the loss of extra resources of each faculty, so that an optimal instance of our common lecturers timetabling is generated among departments. The applied datasets are in terms of satisfying the scheduling requirements in the real world for multi-departments of Islamic Azad University of Ahar branch. [ABSTRACT FROM AUTHOR]

Among the distribution network planning problems, feeder routing is one of the most important and challenging issues. Feeder routing is defined as the optimal determination of the feeders' routes by minimizing the investment cost and satisfying the network constraints. To the best knowledge of the authors, solving feeder routing problem in multi-objective framework is rarely investigated in the previous literature. This paper develops a bi-objective modeling and optimization framework to efficiently solve the feeder routing problem of distribution network considering cost and power losses. Since feeder routing is a complex, high-dimensional and combinatorial optimization problem, a multi-objective differential crow search algorithm (MO-CSAdif) is proposed to obtain a well-distributed and widely spread Pareto front. On a real large distribution network, simulation results confirm the usefulness of the proposed optimization framework. It can be drawn that MO-CSAdif can be a promising method to optimally solve the feeder routing problem. [ABSTRACT FROM AUTHOR]

This paper considers the identification of feedback nonlinear systems with unknown time delay (FNTD) by the chaotic decreasing weight sparrow search algorithm (CWSSA). The CWSSA algorithm uses the improved circle chaos to map the sparrow population, which avoids the problems of clustering and low coverage in the solution space of the initial solution. Thus, the global search ability and convergence speed of the algorithm are improved. By introducing a linear decreasing weight, the risk of the sparrow search algorithm being prone to premature maturity is reduced, and the oscillation phenomenon that is easy to occur near the global optimal solution in the later stage of the algorithm is avoided. Using the search capability of CWSSA and the iterative identification technology, all parameters and the unknown delay of the FNTD system are estimated simultaneously. Finally, the identification results of CWSSA, SSA and PSO are compared through a numerical example. The results show that CWSSA is superior to SSA and PSO in terms of convergence speed and estimation accuracy. The effectiveness of the CWSSA is also verified by the identification of the electro-hydraulic servo position system. [ABSTRACT FROM AUTHOR]

Underactuated systems, such as the double inverted pendulum (DIP) on a cart, are widely used in various applications such as anthropomorphic robot design, assistive robotics, slosh modeling etc. However, the design of a control strategy for such systems is challenging due to their underactuated, nonlinear and unstable nature. This paper addresses the aforementioned challenges by introducing a linear–quadratic-regulator (LQR) controller assisted by the gravitational search algorithm (GSA) to stabilize the pendulums on the cart, while also allowing it to follow the desired movements and overcoming the unmodeled dynamics of the system. The GSA is used to improve the system performances while maintaining stability. The suitability of the proposed method is demonstrated through simulation and experimental studies, including cases with additional weights on the bob and on the cart and external disturbances. The improvements in system performances are shown through a comparative study with the performances of particle swarm optimization (PSO) and quantum PSO-based LQR controllers. The findings corroborate the efficacy of the proposed method in stabilizing the underactuated double inverted pendulum system on a cart in the nominal operating conditions and situations with additional weights and external disturbances. [ABSTRACT FROM AUTHOR]

In recent times, sentiment analysis research has gained wide popularity. That situation causes the importance of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the fuzzy rule-based system (FRBS) with the crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. This study compares the performance of the proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. It tests the model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrate the effectiveness of the proposed model and achieve competitive performance in terms of accuracy, recall, precision, and the F–score. [ABSTRACT FROM AUTHOR]

In this paper, the combination of particle swarm optimization (PSO) and gravitational search algorithm (GSA) is enhanced by the first-order gradient method and a new optimization algorithm is introduced as GPSG. In metaheuristic methods, some search directions are randomly selected and the resulting information gradually progresses toward the optimal solution. Since along the gradient direction usually yields the largest decrease in the desired function, it is added to the GSA and PSO process to allow for faster and more accurate convergence. By integrating the metaheuristic methods with the gradient directions, a powerful method for optimizing functions has been made possible. A novel approach is introduced by merging two metaheuristic methods with the randomly generated steepest decent directions. Numerous examples of unconstrained problems of mathematical functions of CEC2005 and CEC2017 together with some constrained examples of stress and displacement structural design problems have been chosen to demonstrate the reliability and capability of the presented method. Comparison of the numerical results with some methods indicates that the average rank of the proposed technique is better. [ABSTRACT FROM AUTHOR]

This paper investigates permutation flow shop scheduling (PFSS) problems under the effects of position-dependent learning and linear deterioration. In a PFSS problem, there are n jobs and m machines in series. Jobs are separated into operations on m different machines in series, and jobs have to follow the same machine order with the same sequence. The PFSS problem under the effects of learning and deterioration is introduced with a mixed-integer nonlinear programming model. The time requirement for solving large-scale problems type of PFSS problem is exceedingly high. Therefore, well-known metaheuristic methods for the PFSS problem without learning and deterioration effects such as iterated greedy algorithms and discrete differential evolution algorithm are adapted for the problem with learning and deterioration effects in order to find a faster and near-optimal or optimal solution for the problem. Furthermore, this paper proposes a hybrid solution algorithm that is called population-based Tabu search algorithm (TSPOP) with evolutionary strategies such as crossover and mutation. The search algorithm is built on the basic structure of Tabu search and it searches for the best candidate from a solution population instead of improving the current best candidate at each iteration. Furthermore, the performances of these methods in view of solution quality are discussed in this paper by using test problems for 20, 50, and 100 jobs with 5, 10, 20 machines. Experimental results show that the proposed TSPOP algorithm outperforms the other existing algorithms in view of solution quality. [ABSTRACT FROM AUTHOR]

This paper investigates a practical scheduling problem for radiotherapy patients, who are to be scheduled on different devices at different times. The treatment duration is increasing with time because of the continuously decaying effect of the radiation source, which also results in the decline of the serving ability. Therefore, the maintenance activity (replacing radiation source) is necessary to maintain the serving ability of medical institutions. The problem is to determine the schedule of all treatments and also when to have a maintenance activity, so as to minimize the maximum completion time of all the treatments on all devices. The lower bound of the problem is given in this paper. We prove that the optimal solution of the subproblem, i.e., scheduling patients on a single device, is independent of the sequence of the patients and is only related to the division of patients who are assigned before and after the maintenance, and thus, the subproblem can be converted to a two-partition problem. An improved dynamic programming algorithm is proposed to obtain an optimal scheme for this subproblem and its performance is better than other approaches. For multiple-device problem, an effective hybrid algorithm Gaussian crow search algorithm (GCSA) combined with crow search algorithm (CSA) and Gaussian distribution is proposed to assign all patients to different treatment devices. Finally, computational experiments demonstrate the effectiveness and stability of the proposed GCSA which is compared with CSA, simulated annealing (SA) and particle swarm optimization (PSO). The comparison results show that GCSA outperforms other algorithms in a feasible time. [ABSTRACT FROM AUTHOR]

With the rise of network on handheld devices, security of the network has become critical issue. Intrusion detection system is used to predict intrusive packets on network; two-step procedure has been used to predict the intrusion, i.e., feature selection and then classification. Firstly, unwanted and expandable features in data lead to network classification problem which affect the decision capability of the classifiers, so we need optimize feature selection technique. Feature selection technique used in this paper is based on the correlation information known as correlation-based feature selection (CFS). In this paper, CFS's search algorithm is implemented using Chaotic Flower Pollination Algorithm (CFPA) that logically selects the most favorable features for classification referred as CFPA-CFS. Further, hybridization of CFPA and support vector machine classifier is implemented and named as CFPSVM. Finally, novel IDS framework uses CFPA-CFS and CFPSVM in sequence to predict the intrusion. Further, performance of proposed framework is evaluated using two intrusion detection evaluation datasets, namely KDDCup99 and NSL-KDD. The results demonstrate that proposed CFPA-CFS contributes more critical features for CFPSVM to achieve better accuracy compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Inventory management requires thousands or millions of individual transactions each year. Classification of the items influences the results of inventory management. Traditionally, this is usually classified with considering an annual dollar usage criterion but maybe other criteria such as lead time, criticality, perishability, inventory cost, and demand type can be affected on that classification. Inventory items that have more than one criterion are discussed under multi-criteria inventory classification (MCIC) methods in the literature. In this paper, the MCIC problem is considered with two objectives as follows: (1) minimization of total inventory relevant cost and (2) minimization of the dissimilarity index. The proposed Mixed Integer Nonlinear Programming (MINLP) model of the MCIC problem is formulated using Scatter Search Algorithm (SSA). The suggested multi-objective optimization problem is solved using LP-metric, ɛ-constraint and weighted sum method. Pareto optimal solutions are obtained according to these different methods and selected best method by using deviation index. Scatter Search Algorithm provides high-quality solutions within reasonable computation times. The proposed model generated a Pareto frontier solution with the maximum satisfaction level and minimum distance from ideal point. Finally, the proposed model is implemented with two numerical datasets to show the performance of its efficiency and compared our results with other studies in the previous literature. [ABSTRACT FROM AUTHOR]

In the recent years, the optimization problems show that they are a big challenge for engineering regarding the fast growth of new nature-inspired optimization algorithms. Therefore, this paper presents a novel nature-inspired meta-heuristic algorithm for optimization which is called as bear smell search algorithm (BSSA) that takes into account the powerful global and local search operators. The proposed algorithm imitates both dynamic behaviors of bear based on sense of smell mechanism and the way bear moves in the search of food in thousand miles farther. Among all animals, bears have inconceivable sense of smell due to their huge olfactory bulbs that manage the sense of different odors. Since the olfactory bulb is a neural model of the vertebrate forebrain, it can make a strong exploration and exploitation for optimization. According to the odors value, bear moves the next location. Therefore, this paper mathematically models these structures. To demonstrate and evaluate the BSSA ability, numerous types of benchmark functions and four engineering problems are employed to compare the obtained results of BSSA with other available optimization methods with several analyzed indices such as pair-wise test, Wilcoxon rank and statistical analysis. The numerical results revealed that proposed BSSA presents competitive and greater results compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]

Optical character recognition is becoming one of the widely researched areas in recent times. This research paper presents an optimization framework for ancient script recognition using the process of script or character segmentation. The proposed algorithm is based on evolutionary algorithm and capable of handing a continuous script of high-resolution data using concepts of big data. A hybrid combination of group search and firefly algorithm has been proposed in this research work and compared against recent works. Optimal classifications results are observed and recorded in this research paper. [ABSTRACT FROM AUTHOR]

In this paper, the consumption of energy is efficiently balanced using the high searching capability of harmony search algorithm (HSA). However, centrality in finding the route is considered as an utmost challenge in finding the important node in WSN. Hence, the path operator calculus centrality (SPOCC) is used to optimize the centrality problems in routing. The SPOCC finds the main routing path using HSA, and high centrality node is estimated by particle swarm optimization (PSO) algorithm, thereby ensuring optimal routing with reduced energy consumption. The utilization of PSO improves the lifetime of nodes using its dynamic capability. The performance of the proposed hybrid algorithm is evaluated using the various result metrics in smart grid outdoor transmission environment. The results show that the proposed hybrid algorithm obtains better improvement in terms of reduced delay and high residual energy than the existing algorithms. [ABSTRACT FROM AUTHOR]

This paper aims to percolate energy management of microgrid systems by minimizing the generation cost of the same. Energy management of microgrid refers to the optimal sizing and scheduling of the distributed energy resources to reduce the generation cost and pollutant emission. A recently developed crow search algorithm (CSA) is implemented to execute the optimization. The proposed CSA imitates the crows' memory and tactics of hiding and chasing their food. Six renewable integrated microgrid test systems and a total of eighteen different cases are considered for this study. Various practical complexities such as valve point loading effect, combined economic–emission dispatch using price penalty factor method, modeling of the renewable energy sources and energy storage systems are taken into consideration for energy management of the microgrid systems. Results obtained are then compared to a number of different soft computing techniques such as genetic algorithm and particle swarm optimization and the likes to justify the effectiveness of the proposed algorithm. A statistical analysis, viz. Wilcoxon signed-rank test, is performed to prove the superiority of the proposed approach over the various other optimization techniques used in the paper. [ABSTRACT FROM AUTHOR]

In web information retrieval, every single user has a unique contextual objective when searching for information on web blogs. Based on the given query, the task of the web search engine is to fetch the most related information from the collection of web blogs. In order to enhance the searching ability, efficient semantic matching models, richer training, and evaluation resources are required. The conventional keyword-based search algorithms have a minimum efficiency in knowing users' intentions compared to machine learning algorithms. Recently, neural networks are well-recognized in information retrieval due to the ability of vector representation learning. This paper proposes an adaptive fuzzy feedback recurrent neural network-based web blog searching technique which follows inverse filtering (IF) algorithm using Word2Vec representation. Initially, the user query is pre-processed, and then given to the IF for accelerating the search process. Inside the IF, the web blog content is labelled according to their blog information and stored in the hash table, and then relevant contents are extracted by TF–IDF; this result is given to the similarity estimation to obtain the similarity score. Finally, the proposed technique considers the feedback got from users and performs re-ranking by fuzzy-based RNN to achieve rich user intention satisfaction. The implementation is performed in Python, and statistical measures such as accuracy, precision, and recall are utilized for performance evaluation. The results demonstrate that the proposed technique has enhanced accuracy (94%) compared to conventional techniques, namely deep auto-encoder, deep neural networks, and artificial neural networks. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid algorithm is implemented for the system modelling and the optimal management of the micro-grid (MG)-connected systems with low cost. The increasing number of renewable energy sources and distributed generators requires new strategies for their operations in order to maintain the energy balance between the renewable sources and MG. Therefore, an efficient hybrid technique is proposed in the paper. The main objective of the process was the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real-time scheduling. The proposed hybrid technique is to manage the power flows between the energy sources and the grid. To achieve this point, demand response and minimum cost of energy are determined. The proposed hybrid technique is the combined performance of both the gravitational search algorithm (GSA)-based artificial neural network (ANN) and squirrel search algorithm (SSA), and it is named as SOGSNN. This technique is involved with the mathematical optimization problems that necessitate more than one fitness function to be optimized simultaneously. By using the inputs of MG-like wind turbine, photovoltaic array, fuel cell, micro-turbine, diesel generator and battery storage with corresponding cost functions, the GSA-based ANN learning phase is employed to predict the load demand. SSA clarifies the squirrel in optimizing the configuration of MG based on the load demand. The proposed hybrid technique is implemented in MATLAB/Simulink working platform and compared with other solution techniques like ANFASO method. The comparison result reveals that the superiority of the proposed technique confirms its ability to solve the problem. [ABSTRACT FROM AUTHOR]

This paper introduces a new optimization algorithm called electron radar search algorithm (ERSA) inspired by the electron discharge mechanism. It is based on the natural phenomenon of electric flow as the form of electron discharge through a gas, liquid, or solid environment. When the voltage between separated electrodes (anode and cathode) increases, electrons tendency to emission from a low potential state to the higher potential condition is grown up. However, electrons are trying to find the best path with the least resistance in the medium. At each point, electrons evaluate the surrounding environment with a radar mechanism and least resistance path is selected for the next move. Hence, in this paper, a novel developed meta-heuristic algorithm based on the electrons' search approach is presented and the algorithm is benchmarked on 20 mathematical functions with four well-known methods for validation and verification tests. Moreover, the algorithm is implemented in two engineering design problems (tension/expression spring and welded beam design optimization) and the results demonstrate that the ERSA performs more efficiently for solving unknown search spaces and the algorithm found best solution in approximately 95% of the reviewed benchmark functions. [ABSTRACT FROM AUTHOR]

With the development of technology, mobile robots are becoming more and more common in industrial production and daily life. Various rules are set to ensure that mobile robots can move without collision. This paper proposes a novel intelligent optimization algorithm, named fallback beetle antennae search algorithm. Based on the analysis of biological habits, when the creature enters blind alley during the foraging process, it will retreat a distance and then restart the search process. We introduce a fallback mechanism in the traditional beetle antenna search algorithm. In addition, the proposed algorithm possesses the characteristic of low time complexity. It can plan a collision-free path in a short period of time. Moreover, the effectiveness and superiority of the algorithm are verified by simulations in different types of environments and comparisons with existing path planning algorithms. [ABSTRACT FROM AUTHOR]

As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) exhibits promising performance on a wide set of optimization problems, for which the fireworks algorithm has been concentrated on and investigated by researchers recently. This paper aims to improve the performance of the FWA by exploiting updating information of the core firework to guide the algorithm's searching process. Based on this mentality, this paper ameliorated the explosion strategy of core firework of dynamic fireworks algorithm (dynFWA). The proposed algorithm, named dynPgFWA in this paper, improved FWA from two aspects: amplifying the explosion amplitude on the direction on which core firework is updated, and making more sparks which are generated by core firework distributed on this direction to enhance the algorithm's searching ability on updating direction. A numerical experiment on CEC2015 and CEC2017 test suite was implemented to verify the performance of the proposed algorithm. Results of the experiment indicated that dynPgFWA outperformed the compared evolutionary algorithms in the quality of solutions. [ABSTRACT FROM AUTHOR]

Cuckoo search (CS) is an excellent population-based algorithm and has shown promising performance in dealing with single- and multi-objective optimization problems. However, for many-objective optimization problems (MaOPs), CS cannot be directly employed. So far, few paper have been reported to use CS to solve MaOPs. In this paper, we try to propose a hybrid many-objective cuckoo search (HMaOCS) for MaOPs. In HMaOCS, the standard CS is firstly modified to effectively deal with MaOPs. Then, non-dominated sorting and the strategy of reference points are employed to ensure the convergence and diversity. In order to verify the performance of HMaOCS, DTLZ and WFG benchmark sets are utilized in the experiments. Experimental results show that HMaOCS can achieve promising performance compared with five other well-known many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces the Golf Sport Inspired Search (GSIS) algorithm as an evolutionary search method for numerical optimization. Each solution is generated with the aid of the step-length and search direction. The step-length is determined with the aid of the Tait's model of the trajectory of the golf ball, which is a physical model. The search direction is from the current position in the search space toward the position of a different individual or its reflected position. Such a direction determines the movement direction in the optimization process. A crossover operator is introduced to increase exploration at the starting and exploitation at the ending stages of the search. Performance of the GSIS is compared with many algorithms on 23 + 14 unconstrained classic functions, 29 functions of CEC 2017 benchmark suite and six constrained engineering design problems. Experiments indicate that with the aid of its cleverly designed operators, GSIS is able to produce promising results. Besides a cooperative game theoretic approach is introduced, which is able to measure the effectiveness of different operators in reducing the search cost. Such an approach can be used to measure the effectiveness of different operators that an evolutionary or swarm-intelligence algorithm owns. [ABSTRACT FROM AUTHOR]

Under the retailing environment, shelf space-based competition between items with different brands has drawn much attention from practitioners and researchers, the decision for low-temperature stored items on limited shelf spaces are compounding this challenge. This paper discusses the shelf display space partition assignment decision for low-temperature dairy items with two-phase demand in remaining shelf-life to maximize the profit of the shelf. Two-phase demand is assumed for the first time based on the knowledge that consumers' perception on the quality of the dairy item is unequal to its true shelf-life. Moreover, considering shelf partitions in different heights carry different sale gaining potentials, three profit weights are given to the partitions. Herein, a new research model named as SPSPA of shelf profit space partition assignment for the low-temperature stored dairy items is constructed. To effectively solve the proposed SPSPA, a hybrid firefly particle swarm optimization (HFPSO) is developed combing the superiority of particle swarm optimization (PSO) in coarse search and firefly algorithm (FA) in fine search. Numerical experiments have been performed on small- and large-scale cases to verify the performance of the proposed HFPSO algorithm by comparing with genetic algorithm, differential evolution (DE), PSO, and FA based on which management insights are obtained for the shelf space assignment decision. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

Due to the users' mobility, new online applications, as well as low processing power and limited energy of smart devices, traditional cloud computing models could not provide new required services. Cloud service providers improve the quality of their services by moving some servers to the edge of the network and closer to mobile users. Considering the moving nature of users and the heterogeneous service demands in different areas, the optimal placement of servers plays an important role in increasing the quality of service provided to users. However, because of the large number of servers, finding the optimal location of these resources is a serious challenge. In the proposed method of this paper (MES-WG), in the first step, the geographical area of server deployment is divided into smaller sub-regions to reduce the complexity of the problem. Then, by using the WOA algorithm the search agent finds the optimal location of the servers. In the next step, a neural network is used for the local placement of all servers in each area. Finally, game theory is deployed for the convergence of resource placement in all sub-regions. The experimental results show that the proposed method reduces the network latency by 33.5% and also improves the load balance on servers by 28.2%, compared to some of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Over the last decade, the size and complexity of real-world problems have grown dramatically, necessitating more effective tools. Nature-inspired metaheuristic algorithms have proven to be a promising tool for solving such problems due to their performance in a variety of fields. JAYA algorithm is a novel population-based algorithm which could have been able to present reliable results. This is because it does not need any parameters to be set other than the population size and the maximum number of iteration. Despite its positive feedbacks, this algorithm should be modified to witness more efficiency. This paper aims to amend the original version of Jaya to present a high-efficiency version named Powerful Enhanced Jaya (PEJAYA). In other words, the methodology of updating position in Jaya is modified to enhance the convergence and search capabilities. This approach is assessed according to solve 20 well-known benchmark functions, feature selection, and statistical tests. The output results of proposed optimization algorithm are then evaluated by comparing it with other recent algorithms including crow search algorithm (CSA), standard version of JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO) and sine–cosine algorithm (SCA). Solving a real-world problem is another way of checking the efficiency of this approach with other published works. Prompt escape from local minima, superior convergence, and stability demonstrate that the suggested approach is a very powerful instrument that may be employed in a variety of optimization situations. [ABSTRACT FROM AUTHOR]

Basic science is becoming more computing intensive with the incremental need for large-scale computing and storage resources. Cloud computing provides great potentials for hosting and executing scientific applications, which can be represented as workflows for automatic execution and run time provisioning. However, workflow scheduling is highly challenging under dynamic cloud environments since certain runtime QoS. In this paper, we propose an improved swarm search algorithm, i.e., an Owl Search Algorithm embedded with a newly designed Mutation strategy (OSAM) for scheduling workflows with makespan minimized under budget constraints. A population update mechanism, where each particle is updated in term of the impact of current best solution, is modified for an Owl Search Algorithm (OSA) to address discrete sequential optimization problems. We then adjust a step parameter β such that it decreases adaptively with the number of iterations to enhance OSAM's convergence speed. To further increase the diversity of population and enlarge OSAM's global search ability, we embed a mutation strategy into OSA's evolutionary process for better balancing between exploitation and exploration. A series of experiments are conducted to verify the proposed algorithm by comparing with benchmarking algorithms over well-known scientific workflows with different types and sizes through WorkflowSim. Experimental results show that in almost all the cases, the proposed OSAM outperforms the existing algorithms in the solution quality and constraint satisfiability, i.e., it can find near-optimal solutions that meet the tight and loose budget constraints within an acceptable time interval, especially in addressing large-scale applications, e.g., its RSR values are at least 0.10 greater than other algorithms in these cases. Besides, a nonparametric statistical hypothesis test, i.e., Wilcoxon signed-rank test, is applied to the resulting solutions, and the testing results demonstrate that our OSAM is significantly different from its peers. [ABSTRACT FROM AUTHOR]

In microgrids, to have minimal operation cost, optimal power scheduling among distributed energy resources is an essential task. Owing to nonlinearity and complexity of power scheduling problem, it is vital to use a high-performance optimization technique to efficiently solve this problem. To achieve this goal, in this paper, crow search algorithm (CSA) is proposed and improved to optimally solve the power scheduling problem in a microgrid including photovoltaic modules, wind turbines and combined heat and power system. In the improved CSA (ICSA), there are three modifications: (1) oriented differential operator, (2) adaptive flight length and (3) repulsion factor. These modifications help the algorithm to provide a better balance between diversification and intensification. In order to validate the effectiveness of the proposed algorithm, the results obtained by ICSA are compared with the results found by the other algorithms. Over IEEE 37-node feeder, simulation results indicate that the proposed algorithm finds more accurate results than the other methodologies. In comparison with artificial fish swarm, additive increase multiplicative decrease, memory-based genetic algorithm, genetic algorithm, particle swarm optimization with inertia weight (PSOw) and particle swarm optimization with constriction factor (PSOcf), when ICSA is applied, operation cost decreases around 19, 19.7, 0.7, 36, 6.1 and 5%, respectively. [ABSTRACT FROM AUTHOR]

Linear infrastructure projects in congested urban cities require optimal site layout planning. Nevertheless, most of the existing models are developed to address building construction projects with confined boundaries. This paper presents a model for assigning construction facilities locations (CFL) to each work segment in linear infrastructure projects that are characterized by frequent changes of construction site location as the work progresses. The model accounts for the resource transportation cost, land renting cost, relocation cost of CFLs, as well as duration needed for each work segment. The proposed model utilizes a tree search algorithm—Uniform Cost Search (UCS)—that guarantees a global optimal solution. In addition, a pruning technique is utilized to decrease the tree size to reduce the needed computation time. Finally, to account for the stochastic factors (i.e., transportation cost and duration needed per segment), the model is integrated into a Monte Carlo simulation. The proposed model, with the pruning algorithm, is able to solve a 4 trillion-solution space problem in under 3000 ms using an 8 GB Ram, 2 GHz machine. The proposed model can be integrated into the planning and site layout decision making processes for linear infrastructure projects to reduce the overall site layout cost and maintain adequate site conditions while reducing the interruptions in road utilization. Finally, the presented pruning algorithm can be utilized in similar construction engineering problems to efficiently identify global optimal solutions. [ABSTRACT FROM AUTHOR]