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Abstract: During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution using only basic elementary operations. The proposed approach has been developed for a UK paper mill. [Copyright &y& Elsevier]

Now, we have entered the "internet plus" era, and all industries are making use of information technology to seek a longer-term development. For vegetable distribution, we must rely on the power of information technology. Faced with complex global optimization problems, traditional optimization algorithms have many performance limitations, such as complex solution process and easy to fall into local optimization, which can't meet the needs of practical applications. This paper studies the design and application of optimization algorithm for vegetable planting and distribution based on adaptive differential evolution algorithm. The method adopted in this paper is adaptive differential evolution algorithm. In the original differential evolution algorithm, there are three undetermined parameters: population size (NP), variation factor (F) and crossover probability (CR). In the process of evolution, NP takes a fixed value, while the key control parameters F and CR, which affect the performance of the algorithm, will adaptively take different values on [0,1], and the values of F and CR will be recalculated for each individual in each generation. Let F and CR automatically adjust and change in the process of evolution. The research in this paper shows that the algorithm in this paper is effective and suitable for application. [ABSTRACT FROM AUTHOR]

• A robust method is proposed to alleviate the effect of traffic data with outliers. • A comprehensive traffic flow prediction indicator system is established in this paper. • We hybridize polynomial and Gaussian kernel to build nonlinear version of our method. • Parameters of the proposed method are optimized by Fruit Fly Optimization Algorithm. A novel least squares twin support vector regression method is proposed based on the robust L 1 -norm distance to alleviate the negative effect of traffic data with outliers. Although there is some known work for the short-term traffic flow prediction problems, their efficacy depends heavily on the collected traffic data, which are often affected by various external factors (e.g. weather, traffic jam or accident), leading to errors and missing data. This makes it difficult to pick an effective method that accurately predicts the traffic state. As a contribution of this paper, an iterative algorithm is designed to solve the non-smooth L 1 -norm terms of our method; its convergence also proved. Further, a comprehensive traffic flow indicator system based on speed, traffic flow, occupancy and ample degree is utilized in this paper. We also extend the proposed method to a nonlinear version by hybridizing the polynomial kernel and radial basis function kernel, where the weight coefficient of hybrid kernel is determined by the change tendency of traffic data. To promote the prediction performance, the parameters of our nonlinear method are optimized by adaptive fruit fly optimization algorithm. Extensive experiments on real traffic data are performed to evaluate our model. The results indicate that the newly constructed model yields better prediction performance and robustness than other models in various experimental settings. [ABSTRACT FROM AUTHOR]

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates. • Developed distributed parallel mechanism as a method to balances the exploration and exploitation of algorithms. • A proposed distributed parallel firefly algorithm (DPFA) improves the performance of the original FA. • Compared DPFA with other well-known optimization algorithms under the test suite of CEC2013. • DPFA is applied in the control of variable pitch wind turbine to tune PID parameters. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) provides a metaheuristic optimization technique for real-life functions with complex behavior. Inspired by the metaphor of social behavior as exhibited by swarms operating in natural surroundings, the algorithm captures the Emergent Complexity demonstrated by individual components of large groups which work together using simple rules and create complex, yet efficient, adaptive, and self-organizing systems. The paper seeks to rigorously analyze the hyperparameters of the algorithm to find the relative importance of cognitive(personal) vis-à-vis social (swarm) parameters of the algorithm so as to gain a perfect understanding of the importance of exploitation versus exploration parameters thus enabling us to enhance the application efficiency of the algorithm. The paper further suggests and applies a rolling coefficient updating technique to the algorithm parameters so as to use a formal statistical procedure in the initialization and subsequent iterations for updating coefficients rather than leaving it on trial and error or on a practitioner's knowledge. Results suggest that the technique employed provides an enhancement in the efficiency of the algorithm by creating a dynamic Emergent Complexity environment. [ABSTRACT FROM AUTHOR]

In this paper feasibility-preserving genetic operators for hybrid algorithms using TSP solvers for the Inventory Routing Problem (IRP) are studied. The IRP is a problem of jointly optimizing delivery schedules and routes for vehicles transporting products from a supplier to a number of retailers. This optimization problem is highly constrained, because limits on inventory levels as well as the vehicle capacity have to be taken into account. Moreover, the IRP is a generalization of the TSP and solving the TSP effectively is an important part of obtaining good solutions to the IRP. In this paper evolutionary algorithms are used for solving the IRP, but finding good routes is delegated to a state-of-the-art TSP solver. Therefore, genetic operators used in this paper focus on constructing the delivery schedule and not on optimizing the routes. In the experimental part of the paper an evolutionary algorithm using feasibility-preserving genetic operators is compared to the Infeasibility Driven Evolutionary Algorithm (IDEA) that uses the selective pressure to obtain feasible solutions. Presented results suggest that designing good feasibility-preserving genetic operators is important, because allowing the optimization algorithm to generate infeasible solutions and handling infeasibility in IDEA using the selective pressure leads to inferior results. [ABSTRACT FROM AUTHOR]

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering. [ABSTRACT FROM AUTHOR]

Chaotic system parameter modeling is one of the significant topics in the field of non-linear science and therefore has attracted the attention of many researchers in this field. This paper discusses parameter recognition of Fractional Order Financial Chaotic System by employing different metaheuristic algorithms. The implemented algorithms include Artificial bee colony, Grey wolf optimizer, Whale optimization algorithm and Ant colony optimizer. Mean Square Error is employed as objective function to estimate parameters of system under consideration. Results of optimized parameters using the above-mentioned optimization are compared with each other and discussed in the paper. The overall outcome shows that Whale optimization algorithm gives more accurate and robust results with higher convergence rate as compared with other algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for designing stable hybrid L 1 adaptive controller employing lbest topological model of harmony search (HS) algorithm. The proposed design approach guarantees desired stability and simultaneously provides satisfactory tracking performance for a class of non-linear systems. The design methodology for the controller utilizes the meta-heuristic global search feature of HS algorithm and the local search phenomenon of L 1 adaptive control strategy in tandem. The paper also analytically describes the superiority of lbest topological model compared to the conventional HS algorithm in terms of convergence phenomenon, when hybridized with L 1 adaptive control. The proposed hybrid control methodology has been implemented for benchmark simulation case studies and real-time experimentation to demonstrate its usefulness. • Concurrent mode of hybridization of meta-heuristic optimization technique with the L 1 adaptive control structure for process automation. • Choice of L 1 adaption gains according to the merit of the adaptable parameters. • The guaranteed stability of the proposed design with the superior convergence performance of lbest HS algorithm based L 1 adaptive controller. [ABSTRACT FROM AUTHOR]

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (P c) and mutation P m probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods. [ABSTRACT FROM AUTHOR]

Rule-based classification is one of the important tasks in data mining due to its wide applications, particularly in the domains that need to interpret the classification decision such as medical diagnosis. The rule-based classification is a combination of the classification and association rule mining fields which aims at building interpretable classifiers by means of classification rules. This paper presents a novel and efficient sequential covering strategy for Classification Rule Mining to improve the interpretability of classifiers using a Discrete Equilibrium Optimization Algorithm called DEOA-CRM. Our approach benefits from the advantages of associative classification and population-based intelligence. It is inspired by the recent meta-heuristic equilibrium optimization algorithm. New discrete operators defined enable our approach to avoid local solutions and find global ones, improving the exploration and exploitation power in the search space. The proposed DEOA-CRM is tested on a total of 12 test data sets of various sizes and benchmarked with four recent and well-known rule-based classification mining algorithms. The obtained results confirm the efficiency of our algorithm in three chosen measures. Our approach fully deserves its use for classification rules generation to help decision-makers generate accurate and interpretable models. [ABSTRACT FROM AUTHOR]

Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Some important limitations of decision trees are instability, local decisions, and overfitting for this extensive data. The simple, effective and non-convergence nature of the African Buffalo Optimization (ABO) algorithm makes it suitable to solve complex optimization problems. In this paper, we propose the African Buffalo Optimized Decision Tree (ABODT) algorithm to create globally optimized decision trees using the intelligent and collective behaviour of African Buffalos. The modified African Buffalo optimization algorithm is used to create efficient and optimal decision trees. To evaluate the efficiency of the proposed African Buffalo Optimized Decision Tree algorithm, experiments are performed on 15 standard UCI learning repository datasets that are of various sizes and domains. Results show that the African Buffalo Optimized Decision Tree algorithm globally optimizes decision trees, increases accuracy and reduces the size of a decision tree. These optimized trees are stable and efficient than conventional decision trees. [ABSTRACT FROM AUTHOR]

Abstract Capturing an uncontrolled space target is a tremendously challenging research topic. Target capture by a space robot can be well planned according to predicted motion of the target. In this paper, motion prediction of an uncontrolled space target is studied and a motion prediction algorithm is proposed. In the proposed algorithm, firstly a method for identifying the parameters of motion state and inertial property of the target is established; and then through substituting the identified parameters into the dynamic equations of the target, the motion of the target can be predicted as the solution of the equations. In the identification of the parameters, the unscented Kalman filter (UKF) is applied. In order to support the UKF, a method for estimating noise level of the observation data is developed, so our motion prediction algorithm is noise adaptive. A practical convergent criterion is also designed to determine the time when the estimated result of the UKF is accurate enough, such that the predicted motion is credible enough. After that, the accuracy of the prediction is further improved by an optimization method. In the end of this paper, numerical simulations are done to verify the validity of the proposed motion prediction algorithm. Simulation results indicate that the proposed algorithm is able to predict the motion of the target precisely. [ABSTRACT FROM AUTHOR]

K-anonymity is the most widely used approach to privacy preserving microdata which is mainly based on generalization. Although generalization-based k-anonymity approaches can achieve the privacy protection objective, they suffer from information loss. Clustering-based approaches have been successfully adapted for k-anonymization as they enhance the data quality, however, the computational complexity of finding an optimal solution has shown as NP-hard. Nature-inspired optimization algorithms are effective in finding solutions to complex problems. We propose, in this paper, a novel algorithm based on a simple nature-inspired metaheuristic called Black Hole Algorithm (BHA), to address such limitations. Experiments on real data set show that data utility has been improved by our approach compared to k-anonymity, BHA-based k-anonymity and clustering-based k-anonymity approaches. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology, new wireless applications and services are exploding. These new applications and services usually need wider bandwidth and higher speed. The voice of people calling for a fast and convenient network is getting stronger and stronger, and the wireless network must be the main development direction of the network in the future world. In order to make the development of wireless communication technology not limited by limited resources, resource optimization design has always been a research hotspot of scientists. Among them, the optimal resource algorithm can get the theoretical global optimal solution, while the sub-optimal resource allocation algorithm, as a supplement to the former, can be used to reduce costs and improve efficiency when the system structure is too complex. In this paper, a resource optimization method based on sparse clustering algorithm is proposed, and the matching relationship between spectrum resources and the services carried by cognitive terminals is established, so that the effectiveness of collaboration and cognitive systems is verified. [ABSTRACT FROM AUTHOR]

The meta-heuristic algorithms have aroused great attention for controller optimization. However, most of them are inseparable from the explicit system models when addressing a constrained optimization problem (COP). In this paper, we propose a data-driven constrained bat algorithm via a gradient-based depth-first search (GDFS) strategy. In the proposed scheme, the GDFS strategy can predetermine a search space that satisfies some strict constraints (e.g., stability requirements) of the optimized system. Meanwhile, an improved boundary constraint handling method is proposed to limit the exploration process to the predetermined space. In this way, the proposed algorithm can solve the COP by utilizing experimental data from real scenes, thereby relieving the dependence on precisely modeling the complex system. Together with an ɛ -constraint-handling method, the bat algorithm is employed to seek the global optimum of the COP. The search performance is enhanced by the designed linear-varying elite layer-based local search and a social learning-based walk mechanism to dynamically balance exploration and exploitation. The convergence is ensured based on the criteria of the stochastic optimization algorithm. Experimental results on a servo drive system and benchmark test functions verify the effectiveness of the proposed algorithm. • Design a data-driven optimization method for the constrained controller tuning. • Design a gradient-based depth-first search strategy for strict constraints. • BA integrates a novel search mechanism to prevent premature convergence. • An improved boundary constraint handling scheme is proposed. [ABSTRACT FROM AUTHOR]

An accurate and efficient control of the Maximum Power Point Tracking (MPPT) and pitch angle for the Wind Turbine (WT) system can provide more energy while also protecting the material. Thus, considering these economic aspects, many controllers are proposed to guarantees their performances. The most studied strategy is the classical PID controller. However, its performance is limited due to the nonlinear model and the difficult wind speed form, which inflict many uncertainties. To solve these drawbacks, an advanced controller named Type-2 Fractional Order Fuzzy PID (T2-FOPID) controller is suggested in this paper to improve the exigence controls. Meanwhile, an efficient optimization technique named the Salps Swarm Algorithm (SSA) has been introduced in order obtaining the best controller parameters. The efficiency of the proposed controller is tested to the 10 kW-WT under various wind speed scenarios. The derived results explicitly indicate that the proposed strategy outperforms the PID controller and other controllers, in terms. the minimum of error, the overshoot and the settling time. • The pricipal controls of the wind turbine system are are the MPPT and the pitch angle. • The authers proposed the Type-2 Fractional order PID controller based on salps swarm algorithm. • The results proved the superiority of proposed controller than the classical PID controller and others are largely suggested in the literature. [ABSTRACT FROM AUTHOR]

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Today, the Structural Health Monitorin (SHM) methodology is the main way to deal with the detection and identification of damages in a range of engineering sectors, mainly in the military and civil aerospace industry. The need to monitor damages and failures in increasingly complex structures has led to the development of various detection techniques. Identification of damages through intelligent signal processing and optimization algorithms are particularly emphasized. The methods discussed here are mainly elaborated by the evaluation of modal data due to the great potential of their application. Moreover, the optimization of damage identification is approached through methods of optimal positioning of sensors for the acquisition of the data that must be evaluated so that conclusions can be made. This article discusses the use of computational and intelligent techniques for structural monitoring in the form of a review with emphasis on composite materials. Despite the excellent mechanical performance already known about composite materials, they have a weak point. While damages, in a metallic material are easily visible (in some cases), composite materials often have the superficial appearance as if in perfect condition, when, inside, there are serious damages. This paper can be seen as a guideline or a starting point for developing and improving SHM systems. The contents of this paper aim to help engineers and researchers find a starting point in developing a better solution to their specific structural monitoring problems, either by inverse methods, pattern recognition, and intelligent signal processing. [ABSTRACT FROM AUTHOR]

Let B be a set of b blue points and R be a set of r red points in the plane. In this paper we study the problem of finding rectangles that contain the maximum number of blue points without containing any red points, known as the maximum-box problem . First we study this problem for axis-aligned rectangles, and propose an exact worst-case optimal O ( r 2 + r b + b log ⁡ b ) time algorithm using O ( r + b ) space to find all maximum boxes. We also provide a 2-approximation algorithm running in O ( ( r + b ) log ⁡ ( r + b ) ) time and using O ( r + b ) space to find a single maximum box in the axis-aligned case. Then we generalize the exact algorithm for the axis-aligned case to find all arbitrarily oriented maximum boxes leading to a worst-case optimal O ( ( r + b ) 2 ( r + log ⁡ b ) ) time algorithm using O ( ( r + b ) 2 ) space to solve the problem. We conclude the paper by discussing time and space trade-offs. Our results improve the previously best known solutions to the maximum-box problem. [ABSTRACT FROM AUTHOR]

This paper presents optimization techniques for automatic personality recognition (APR) based on Twitter in Bahasa Indonesia, the mother tongue of Indonesians. Foremost, we discuss Twitter and its utilization as a resource for many types of research. Several previous studies have been attempted to predict users’ personality automatically. However, only a few of them have done their research for Bahasa Indonesia data. Therefore, this paper discusses the optimization of APR in Bahasa Indonesia. We evaluate a series of techniques implementing hyperparameter tuning, feature selection, and sampling to improve the machine learning algorithms used. The personality prediction system is built on machine learning algorithms. There are three machine learning algorithms used in this study, namely Stochastic Gradient Descent (SGD), and two ensemble learning algorithms, Gradient Boosting (XGBoost), and stacking (super learner). By implementing this series of optimization techniques, the current study’s evaluation results show huge improvement by achieving 1.0 ROC AUC score with SGD and Super Learner. [ABSTRACT FROM AUTHOR]

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. This paper presents a novel spam classification technique that integrates the Harris Hawks optimizer (HHO) algorithm with the k-Nearest Neighbors algorithm (k-NN). The Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by Harris' Hawks' cooperative relations and surprises pounce pursue technique in nature. According to empirical results on the dataset, the suggested model can handle high-dimensional data (Spambase). The suggested model's spam detection accuracy is compared to the numerous algorithms, including the Binary Dragonfly Algorithm (BDA), Equilibrium Optimizer (EO), Teaching-Learning-based Optimization, Seagull Optimization Algorithm (SO), and Marine Predators Algorithm (MPA). We found that the proposed technique trumps the other spam detection techniques investigated in this study in terms of classification accuracy. The experimental results showed that the proposed technique accuracy reaches %94.3. [ABSTRACT FROM AUTHOR]

The network structure exhibits a variety of changes over time. Fusing this structure and the development of communities in dynamic networks plays an important role in analyzing the evolution and development of the entire network. How to ensure the division of the community structure in social network big data, as well as ensure the continuity of the community between the current time and previous time period, are issues that need to be explored. This problem can be solved by fusing the three characteristics of temporal variability, stability, and continuity in dynamic social network communities, and by adopting the multi-objective optimization method to detect community structures in dynamic networks. The probability fusion method is added to the initial step of the algorithm to generate suitable network partitions and ensure fast convergence and high accuracy. Two neighboring fusion strategies are proposed that are suitable for communities: the neighbor diversity strategy and the neighbor crowd strategy. These two strategies make different changes to the candidate network partitions. A continuity metric for dynamic community evolution is formulated to compare the similarity of the dynamic network communities of two consecutive time steps. Experiments on synthetic datasets and actual datasets prove that the proposed method in this paper provides better performance than existing methods. [ABSTRACT FROM AUTHOR]

In this paper, the authors analyze the algorithm optimization and anomaly detection simulation based on extended jarvis-patrick clustering and outlier detection. We perform detection by using the jarvis-patrick graph-based clustering method. After that, to further improve the false alarm rate (FAR) of the algorithm, we use an extra outlier detection step combined with our proposed EJP to create a new anomaly detection method called LD-EJP. Using LD-EJP, the false alarm rate improved much (experiments show that the false alarm rate can reach 4.1% while the best JP clustering can reach is 7.4%). Then, we tested LD-EJP against two other anomaly detection methods using k-means and LGCCB, showing that our algorithm has a better detection rate and false alarm rate than these two clustering-based anomaly detection methods. In addition, the detection rate and false positives of the algorithm also have some room for improvement. In the labeling process, the proportion of anomaly clusters to normal clusters needs to be manually adjusted to find a better detection rate. In addition, the detection rate we chose can consume some of the detection rate gained in extended JP clustering to have the LD-EJP obtain a better FAR. Therefore, our future work contains finding or proposing another outlier detection algorithm with better performance than our LD-EJP method. [ABSTRACT FROM AUTHOR]

Particle Swarm (PSO) and Bacterial Foraging (BF) Optimizers are two widely used optimization techniques. A proper combination of these two algorithms would improve their search capability while minimizing their shortcomings, such as parameter dependency and premature convergence. This paper presents a hybrid optimization algorithm that combines PSO and BF (HPSBF) to ensure security and the system's stability following faults and disturbances. The formulated objective function is claimed to be innovative and straightforward. The set objectives are to minimize the dropped load by shedding relays while maximizing the lowermost swing frequency. The optimal operation of Under-Frequency Load-Shedding (UFLS) Relays is driven by the HPSBF technique as a bounded optimization with bounds representing the limits of the system's state variables. The viability of the HPSBF is verified against conventional-, PSO-, and BF-UFLS approaches. The standard IEEE 9-bus and IEEE 39-bus systems are exploited to examine the response of the developed UFLS techniques. The tested systems are exposed to various operational scenarios such as loss of power plants and a considerable abrupt load increase. The DigSilent power factor software is used to simulate the IEEE 9- and 39-bus systems, while MATLAB code was implemented to obtain optimal operational points for the implemented algorithms. The HPSBF accomplished the uppermost swing frequency and the lowermost quantity of the disconnected load. Furthermore, the computational times of HPSBF are equivalent to those of the PSO. [ABSTRACT FROM AUTHOR]

In the cloud computing environment, scheduling algorithms show the vital role of finding a possible schedule of the tasks. Extant literatures have shown that the task scheduling problem is NP-Complete as the objective is to obtain the minimum overall execution time. In this paper, we address the problem of scheduling a set of l tasks with a set of | G | groups to a set of m clouds, such that the overall layover time is minimized. Note that overall layover time is the sum of the timing gaps between paired tasks. Here, we present a pair-based task scheduling algorithm for cloud computing environment, which is based on the well-known optimization algorithm, called Hungarian algorithm. The proposed algorithm considers an unequal number of tasks and clouds, and pairs the tasks to make the scheduling decision. We simulate the proposed algorithm and compare it with three existing algorithms, first-come-first-served, Hungarian algorithm with lease time and Hungarian algorithm with converse lease time in twenty-two different datasets. The performance evaluation shows that the proposed algorithm produces better layover time in comparison to existing algorithms. The proposed algorithm is analyzed theoretically and shown to require O (kpl 2) time for k iterations, p repetitions and l tasks. [ABSTRACT FROM AUTHOR]

In the electrical power system optimal power flow is a solution for power management with multi variable equality and inequality constraints. Different algorithms were used to solve the optimum power flow (OPF). This paper proposes the optimal power flow using efficient and reliable optimization technique of enhanced sine cosine algorithm (ESCA). The proposed algorithm used to solve the multi objective power flow function problems such as generating cost, losses, emission of power plant and to improve the voltage stability. In this IEEE standard 30 bus system with four different multi objective functions have been tested and results obtained by Enhanced sine cosine algorithm are compared with other evolutionary algorithms. The results show that ESCA gives better results than other techniques in terms of convergence speed and global optimal solutions. [ABSTRACT FROM AUTHOR]

Multilinear model predictive control (MLMPC) can regulate a nonlinear process with wide operating regions based on a set of linear models. Although online computational cost is reduced compare to nonlinear MPC (NMPC), it is difficult to obtain a reliable full nonlinear model or set of linear models in practice. In this paper, we propose a combination of MLMPC with differential dynamic programming (DDP), so that the system can be controlled offset-free in the absence of a full nonlinear model. DDP is a 'trajectory-centric' optimization technique that solves nonlinear optimal control problems. The trajectory can be optimized even if the full model for the system is unknown, because DDP uses only the gradients around the visited trajectory, which is easily obtained by input excitations. Moreover, the gradient information can provide linear models in the subsequent MLMPC step. In the proposed scheme, a novel model selection based on gap metric and weighting method are employed for MLMPC. We prove the offset tracking property of DDP assisted MLMPC. A continuous stirred tank reactor (CSTR) process is studied to demonstrate the effectiveness of the proposed algorithms. Simulation studies show that CDDP designed by the proposed algorithm improves the trajectory over iterations, and the resulting MLMPC achieves offset-free tracking property regardless of an initial point and a set-point in the operating region. • Data-driven DDP is proposed to find the optimal trajectory without models of nonlinear systems. • LTV model is obtained from Data-driven DDP and exploited for the model of MLMPC. • A novel clustering method based on gap metric is proposed to choose linear models for MLMPC. • MLMPC with a novel prediction-based weighting method is proposed and its stability is discussed. • The proposed MLMPC strategy achieves offset-free control over large operating ranges. [ABSTRACT FROM AUTHOR]

This paper considers the cooperative attack with multiple missiles on a stationary target and a two-step procedure is proposed. First the states of all missiles are guided to achieve minimum time consensus, and then, by using the proportional navigation guidance law with identical navigation gain each missile arrives at the target simultaneously or sequentially. Among which, the strategy to realize state consensus in minimum time is the focus of the issue. In the paper the coordination algorithms are developed based on bang–bang control strategy due to its simply structure, subsequently, it is proved that the time optimal consensus problem can be transformed into a parameter optimization problem. Besides, the proposed schemes are able to significantly reduce the number of parameters to be optimized and finally solve the problem with little computation cost. Different from the impact time control methods, the strategy for state consensus does not demand the information of time-to-go. Meanwhile only the initial values of missiles' states are required for the coordination algorithms which lead to less communication burden. Two different examples are provided to demonstrate the validity and efficiency of our proposed strategy. [ABSTRACT FROM AUTHOR]

This paper presents a runner-root algorithm (RRA) for electric distribution network reconfiguration (NR) problem. The considered NR problem in this paper is to minimize real power loss, load balancing among the branches, load balancing among the feeders as well as number of switching operations and node voltage deviation using max-min method for selection of the final compromised solution. RRA is equipped with two explorative tools, which are random jumps with large steps and re-initialization strategy to escape from local optimal. Moreover, RRA is also equipped with an exploitative tool to search around the current best solution with large and small steps to ensure the obtained result of global optimization. The effectiveness of the applied RRA in both single- and multi-objective has been tested on 33-node and 70-node distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the applied RRA can be an efficient method for network reconfiguration problems with single- and multi-objective. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective optimization (EMO) algorithms are actively used for answering optimization problems with multiple contradictory objectives and scheming interpretable and precise real-time applications. A majority of existing EMO algorithms performs better on two or three objectives non-dominated problems; however, they meet complications in managing and maintaining a set of optimal solutions to multi-objective optimization problems. This paper proposes a search space-based multi-objective evolutionary algorithm (SSMOEA) for multi-objective optimization problems. To accomplish the potential of the search space-based method for solving multi-objective optimization problems and to reinforce the selection procedure toward the ideal direction while sustaining an extensive and uniform distribution of solutions is our key objective. To the best of our knowledge, this paper is the first attempt to propose a search space-based multi-objective evolutionary algorithm for multi-objective optimization. The experimental setup used showed that the proposed algorithm is good and competitive in comparison to the existing EMO algorithms from the viewpoint of finding a scattered and estimated solution set in multi-objective optimization problems. SSMOEA can achieve a good trade-off between search space convergence and search space diversity in the appropriate experimental setup. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to review the practice of the theory of inventive problem solving (TRIZ) in Green Supply Chain (GSC) problems and to identify new research challenges focusing on the question: “To what extent is it necessary to evolve TRIZ tools, methods and theoretical grounding for addressing GSC inventive problems?” First, a review of the past contributions of TRIZ based methods to GSC problem resolution is presented. As the result of the papers review did not provide a comprehensive understanding of the limitations and areas of potential application of TRIZ in GSC, three experiments were conducted to complete the literature review, in order to provide a more comprehensive answer to the posed question and identify research challenges. The experiments addressing GSC problems were also conducted to explore to what extent the more mature meta-methods of classical TRIZ, namely ARIZ 85 A, C and the related sub-methods, can be used as in GSM problems. The examples were chosen to explore types of GSC problems that were not yet addressed with TRIZ. The experiment results highlight limitations on the use of the TRIZ in GSC inventive problems, which were not mentioned in the GSC literature. Moreover it highlights the limitation of using the more mature meta-methods of TRIZ (ARIZ 85A and ARIZ 85C) when the conflict to overcome contains more than two evaluation parameters and one action parameter. Finally, research challenges to overcome the limitations and to improve the use of TRIZ in GSC inventive problems are stated. Among them, methods for quickly establishing the existence of classical TRIZ contradictions or for informing the problem solver when no TRIZ contradictions are present in a given inventive problem in GSC should be proposed. Such methods would permit determining whether ARIZ 85C could be used and avoid a long and fruitless search for a system of contradictions. Find alternatives to the algorithms proposed in the past to be able to establish the generalized contradictions of inventive problems. Make evolve meta-methods ARIZ 85C or substitute it with methods which can address the inventive problems that cannot be treated by ARIZ 85C. [ABSTRACT FROM AUTHOR]

Determination of the creep model parameters is a challenging task particularly when a non-linear elastic visco-plastic (EVP) model is adopted, mainly due to the limited test duration as well as the assumption of the reference time. Therefore, this paper presents an innovative numerical solution to find the EVP model parameters applying the trustregion reflective least square optimization algorithm. The developed approach involves several available laboratory consolidation test results in the optimization procedure with the adopted commencing time to creep as a unit of time. In this paper, the laboratory results of Ottawa clay were employed to demonstrate the limitation of the recent method to obtain model parameters. Furthermore, the developed method is verified against Skå-Edeby clay in the laboratory conditions. The EVP model parameters are obtained by applying the developed method to the available laboratory consolidation results of clay samples. The analysis results of vertical strains and excess pore water pressures demonstrate that the developed method can be a feasible tool to estimate the settlement properties of clays. [ABSTRACT FROM AUTHOR]

It is a common occurrence nowadays for a network to be gigantic in size and complex in architecture. Network navigation thus faces many new problems in terms of routing and resource utilization, one of which arises in multi-domain networks. This paper focuses on the Inter-Domain Path Computation under Node-defined Domain Uniqueness Constraint (IDPC-NDU) problem, whose purpose is to find the shortest path between two nodes in a network under a constraint that such a path is only allowed to traverse each domain at most once. Considering that this is an NP-Hard problem, an approximate approach is more practical than an exact one. Meanwhile, Particles Swarm Optimization Algorithm (PSO) has been long known for its powerful ability to discover near-optimal solutions in a reasonable time; however, its application in discrete search space is limited. Therefore, we decide to use the multi-population framework, a sub-branch of multitasking optimization which allows information exchange not only between problems but also between different optimization heuristics, to improve upon the basic PSO method. Specifically, this paper introduces a hybridization between PSO and Variable Neighborhood Search (VNS). The encoding and decoding method are created specifically for the IDPC-NDU problem and for the PSO algorithm, and VNS serves to enhance further the algorithm's ability to escape local optima. Experiments are carried out to prove the new algorithm's efficacy, especially in the context of similar multitasking methods. • Propose a multi-tasking technique for IDPC-NDU. • Propose novel encoding and decoding methods for the IDPC-NDU problem. • Integrate PSO and VNS into a multi-population-based approach to solve the IDPC-NDU problem. • Evaluate the efficiency of the proposed algorithm on various instances. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

This paper considers one of the methods to account for the local information on the objective function in Lipschitz global optimization problems. In the course of solving such problems, an issue arises regarding estimating the Lipschitz constant of the objective function arises. According to the classic scheme, this constant is a single estimate for the whole search domain. The method of accounting for local properties is based on building estimates of Lipschitz constants for the search subdomains, and has previously been investigated for the one-dimensional case. For solving multidimensional problems, dimension reduction is applied. A multidimensional optimization problem is reduced to a one-dimensional problem, for which the objective function satisfies the Hölder condition. In this paper, the application of building local Hölder constant estimates in reduced multidimensional optimization problems using the same scheme as is used in one-dimensional Lipschitzian problems is considered. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust supervised subspace learning (RSSL) method for output-relevant prediction and detection against outliers. RSSL learns the robust subspaces by optimizing a joint problem over both the prediction of output and the reconstruction of input. To this end, the learned subspaces/data representations are informative, i.e., they are encapsulated with the critic information related to both the input and output, and thus can benefit the following tasks of output-related modeling and detection. Besides, we separate sparse items from the raw measurements to suppress the effects of outliers. An efficient optimization algorithm is designed to solve the optimization problem of RSSL. We further conduct post orthogonal decomposition upon the subspaces provided by RSSL so that the trimmed subspaces are more suitable for output-related detection. The efficacy of the proposed method is extensively verified by synthesis data and benchmark data. • We learn robust latent subspaces by a joint optimization of input and output, where the negative effects of outliers are suppressed by separating sparse items from the original data. • We develop an efficient algorithm to solve the proposed formulation of RSSL. The theoretical and empirical analysis demonstrate the effectiveness of the designed optimization algorithm. • The proposed RSSL is competent to the tasks of output prediction and output-related detection, where, for the latter task, post-decomposition is performed to further trim the subspaces generated by RSSL. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid algorithm was proposed for multi-objective optimization design with high efficiency and low computational cost based on the Gaussian process regression and particle swarm optimization algorithm. For the proposed method, the global performance indices, including regular workspace volume, global transmission index, global stiffness index, and global dynamic index were considered as objective functions. First, the multi-objective optimization problem considering the boundary conditions, objective, and constraint functions was constructed. Second, the Latin hypercube design was regarded as the design of experiment to obtain the computer sample points. Besides, the high-precision objective-function values were obtained by increasing the node density in the workspace at these sample points to provide sufficient information for the mapping model. Third, the Gaussian process regression was proposed to build the mapping model between the objective functions and the design parameters, thus reducing the computational cost of global performance indices. Cross-validation and external validation were adopted to verify the mapping model. Finally, the hybrid algorithm combined with the Gaussian process regression and particle swarm optimization algorithm was proposed for multi-objective optimization design. The 2PRU-UPR parallel manipulator was taken as a case to implement the proposed method (where P was a prismatic joint; R a revolute joint; U a universal joint). The comparison from the back propagation neural network, multivariate regression, and Gaussian process regression mapping models showed that the Gaussian process regression model had higher accuracy and robustness. The proposed hybrid algorithm saved 99.84% of computational cost compared to using the particle swarm optimization algorithm. The Pareto frontier of the multi-objective optimization problem of the 2PRU-UPR parallel manipulator was also obtained. After optimization , the performance indices were significantly improved. • A mapping modeling between objective functions and design parameters based on Gaussian process regression is proposed. • A multi-objective optimization design method combined with the mapping model and particle swarm algorithm is proposed. • The proposed method can significantly reduce the computational cost for the high computation-intensive design problems. • A study on the comparison of the robustness and accuracy among the three mapping models is carried out. • The three mapping models are multivariate regression, BP neural network, and Gaussian process regression models. [ABSTRACT FROM AUTHOR]

Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective Jaya algorithm (TDRM-Jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-Jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method. [Display omitted] • A multi-objective optimization model of cement calcination process is developed. • A TDRM-Jaya algorithm is proposed applied to solve the optimization model. • The multi-objective collaborative optimization using data-driven model is studied. • Computational results show that the expected optimization effect can be achieved. [ABSTRACT FROM AUTHOR]

within constraint satisfaction problems (CSPs), in this paper we introduce a honeybee algorithm that uses new template concept for image segmentation. Our approach uses the guided local search and the graph theory to achieve an improved image segmentation. Compared to previous well known heuristics, our approach is giving better experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• The significant and impressive performance of the Chimp Optimization Algorithm (ChOA) has been a great inspiration for this paper to study on its multi-objective version called MOChOA. • A memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. • Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. The Multi-Objective Chimp Optimization Algorithm (MOChOA), a multi-objective variation of the recently proposed ChOA, is developed in this research to address multi-objective optimization issues in various engineering problems. The optimization community has recently been offered numerous evolutionary and meta -heuristic optimization strategies for tackling optimization challenges. When analyzing multi-objective optimization (MOO) problems, these approaches frequently produce poor solutions because they do not accurately estimate the Pareto optimal solutions or increase the distribution across all objectives. The significant and impressive performance of the ChOA has been a great inspiration for this paper to study on its multi-objective version called MOChOA. In this approach, a memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. According to results, MOChOA can provide competitive results and outperform well-known intelligent algorithms such as ANSGAIII, ARMOEA, dMOPSO, EFRRR, IBEA, SPEA2SDE, SPEAR, and tDEA. Alongside the remarkable performance of the proposed algorithm, its light-weight structure causes a fast execution. The proposed MOChOA can be applied in various multi-objective optimization application, including engineering, science, chemical processes, economics and logistics when appropriate decisions must be made in the face of competing objectives. [ABSTRACT FROM AUTHOR]

• This paper develops a data aggregation technique using optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally data aggregation. • At first, the WSN-based IoT nodes are simulated, which are subjected to multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to base station. • In base station, the data aggregation is done using proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. • Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in WSN-based IoT network. The Internet of Things (IoT) is emerging that acquired the interest of several researchers, as it inspires each object to offer services and data to users by collaborating with each other. The Wireless sensor network (WSN) is a major constituent of IoT in transmitting data. Various wireless sensor-based protocols are developed for path selection. This paper develops a data aggregation technique using an optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally, data aggregation. At first, the WSN-based IoT nodes are simulated, which are subjected to a multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to the base station. In the base station, the data aggregation is done using the proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using the Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in a WSN-based IoT network. The developed DECSA+HABLMS offered superior performance with minimal prediction error of 0.0147. [ABSTRACT FROM AUTHOR]

Optimal coordination and settings of directional overcurrent relays (DOCRs) are crucial task to ensure reliable and effective protection in power system networks. The settings of DOCRs are plug setting current (PSC) and time multiplier setting (TMS). The DOCRs coordination is well-known as a highly constrained nonlinear optimization problem. Its complexity in terms of non-linearity increases along with the network size increment. This paper proposes a hybrid optimization algorithm which consists of Firefly Algorithm and Linear Programming (FA-LP) to relax the search space by linearizing the equation of the DOCRs coordination to attain an optimal solution. Furthermore, this paper also considers a mixed type of IEC relay characteristics to attain effective relay operation. The proposed method is tested on the IEEE 8-, 15- and 30-bus test systems. The results show the reduction of total relay operating time between 15.6% and 85.5% as compared to other techniques in the literature and also being verified using the ETAP software. [ABSTRACT FROM AUTHOR]

Both the species strategy and the memory scheme are efficient methods for addressing dynamic optimization problems. However, the combination of these two efficient techniques has scarcely been studied. Thus, this paper focuses on how to hybridize these two methods. In this paper, a new swarm updating method is proposed to enhance a representative species-based algorithm, i.e., SPSO (Species-based Particle Swarm Optimization), and the new algorithm is named MSPSO. MSPSO has two characteristics. First, the number of replaced particles in the current swarm is set adaptively according to the number of species. To not substantially destroy the exploitation capability of each species, no more than one particle in each species is replaced by the memory. Second, the retrieved memory particles are categorized according to their fitness values and their distances to the seed of the closest species. Aimed at enhancing the search in both promising areas and existing species, each category is processed by different operations. The MPB, Cyclic MPB and DRPBG are used to test the performance of MSPSO. Experimental results demonstrate that MSPSO is competitive for dynamic optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to determine the optimal location and sizing of Distribution Generation (DG) and Distribution STATic COMpensator (DSTATCOM) is analyzed, and the objective function is formulated for minimizing power loss, operational costs and voltage profile enhancement of the system subjected to equality and in equality constraints. Loss sensitivity factor (LSF) is used to pre-determine the optimal location of DG and DSTATCOM. The Bacterial Foraging Optimization Algorithm (BFOA) is proposed to determine the optimal size of the DG and DSTATCOM. In this paper, the DG and DSTATCOM are simultaneously allocated in radial distribution system and it is analyzed with different load models. To check the feasibility, the proposed method is tested on IEEE 33-bus and 119-bus radial distribution system and the results were compared with other existing technique. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid algorithm of Nelder–Mead and scatter search algorithms called SSNM. Numerical results for some known test problems with varying dimensions from 2 to 100 variables prove its great performance for a bounded or constrained problem. This paper also has a considerable contribution to the joint economic lot sizing (JELS) problem literature. This problem is considered for a two-stage supply chain with price-sensitive demand for geometric, geometric-then-equal size and optimal shipment policies. Solution procedures are also developed which use the SSNM algorithm in their steps. These models and solution procedures are novel in the JELS literature. [ABSTRACT FROM AUTHOR]

The Flower Pollination Algorithm (FPA) is a novel bio-inspired optimization algorithm that mimics the real life processes of the flower pollination. In this paper, we review the applications of the Single Flower Pollination Algorithm (SFPA), Multi-objective Flower Pollination Algorithm an extension of the SFPA and the Hybrid of FPA with other bio-inspired algorithms. The review has shown that there is still a room for the extension of the FPA to Binary FPA. The review presented in this paper can inspire researchers in the bio-inspired algorithms research community to further improve the effectiveness of the PFA as well as to apply the algorithm in other domains for solving real life, complex and nonlinear optimization problems in engineering and industry. Further research and open questions were highlighted in the paper. [ABSTRACT FROM AUTHOR]