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Due to the high labour costs in industrialised countries and the increasing demands on the quality of plastic moulded parts produced by the injection moulding process, companies are challenged to increase their production efficiency. Data-based approaches provide an opportunity for systematic and objective process set-up and control. Especially the process set-up can be a tedious, iterative and complex task which can consume a significant amount of time in the preparation of mass production. Artificial neural networks (ANN) can be used by evolutionary algorithms to search for suitable machine setting parameters for the desired part and process quality features. This often results in locally optimal solutions, which do not guarantee the best possible machine settings. Invertible neural networks (INN) could be a solution as with the help of these models, globally optimal solutions may be identified for definitive optimisation as the entire solution space for given quality parameters can be mapped in short time. In our approach, data for the implementation of the INN is collected in practical tests. The resulting weight and several geometric dimensions of the parts are determined as quality parameters. Based on these data, ANN are trained and utilized for the generation of synthetic data for the training of the INN. The creation of synthetic data is necessary because INN have millions of trainable parameters and therefore degrees of freedom which need to be determined using a bigger amount of training data in comparison to traditional feedforward ANN. In this paper, two different databases with a size of 10,000 samples each are being investigated. The two databases differ in the distributions of holding pressure and cooling times in order to investigate the influenceability of the prediction quality of the INN by training data. The sample settings for the injection velocity, packing pressure, mould temperature and melt temperature are uniformly distributed. Two different types of conventional and conditional invertible neural networks are implemented based on literature recommendations. Measured on validation data, the model quality (R² value) for the prediction of the part weight is 0.995 and 0.975 for the part dimensions. The machine setting parameters proposed by the INN can be analysed in regard to their impact. The holding pressure time is identified as the machine setting with the greatest influence on both part weight and part dimension. The predictions of the INN are validated in practical experiments with an R² value of 0.997 for the part weight. The weight of the parts produced with the proposed machine settings deviates on average by 0.24 % from the target. [ABSTRACT FROM AUTHOR]

The convoluted and dynamic nature of cancer growth makes it difficult to predict long-term cancer survival. Evolutionary deep learning techniques have been growing in popularity as efficient and accurate prediction techniques in recent years. Long-term cancer survival prediction has been developed to utilize evolutionary deep-learning techniques. This research combines the power of evolutionary algorithms using Particle Swarm Optimization (PSO) with some deep learning techniques to improve the performance and accuracy of the deep learning models by optimizing its parameters and minimizing the error between the predicted and actual outputs. Overall, combining evolutionary algorithms with deep learning models for cancer survival analysis prediction demonstrates the potential to significantly improve the accuracy and efficiency of cancer survival predictions, leading to better treatment decisions and improved patient outcomes. This paper uses the gbsg2 survival analysis dataset, a well-known dataset commonly used in survival analysis studies. [ABSTRACT FROM AUTHOR]

The efficiency of the systems for the automated formation of artificial neural networks is beyond any disputes. Such systems make it possible to automatically generate ready-to-work models without participation of a domain expert while creating a model. The present paper considers a modification of the evolutionary algorithm for the automated formation of artificial neural networks of direct propagation, which helps build recurrent neural networks. The peculiarity of the algorithm lies in the network structure encoding in the form of a tree allowing the network structure to be represented more compactly, that well correlates with text analysis tasks, the dimensions of which vary from hundreds to thousands of features. To test the approach, the problems of classifying textual information were solved. The averaged test results as well as some of the obtained models are presented to demonstrate the approach. It is showed that the algorithm successfully builds models with a recurrent structure. This approach can be potentially improved by adding various recurrent links. [ABSTRACT FROM AUTHOR]

The Hybrid Flow Shop Scheduling (HFS) topic has received wide attention regarding problem modeling and solution techniques. The HFS problem has been extensively researched due to its importance and complexity. This paper presents an optimization scheme for a case study on minimizing makespan and energy consumption in a hybrid flow shop scheduling problem. The HFS consists of a few productions line with several parallel machines in one or more stages. The significant difficulty is assigning work to specific devices at different locations. A case study was implemented with twenty-seven jobs in four stages comprising bending machines, lathe machines, milling machines, and wire cut machines. The (EE-HFS) optimization has been carried out using Multi Objectives Dragonfly Algorithm (MODA). The optimization result was compared with well-established algorithms, the Pareto Envelope-based Selection Algorithm II (PESA2), Multi Objectives Evolutionary Algorithm Based on Decomposition (MOEA/D), and new algorithms, Multi Objectives Grasshopper Optimization Algorithm (MOGOA) and Multi-Objective Ant Lion Optimizer (MOALO). Five metrics were employed for comparison purposes, Error Ratio (ER), Spacing (SP), Maximum Spread (MS), computational speed (CS), and Generational Distance (GD). The MODA outperformed other algorithms based on the convergence metric and was on par with different algorithms for spacing and maximum spread metrics optimization results. In conclusion, these results are significant for Hybrid flow shop scheduling regarding the energy utilization model and MODA's excellent potential to be implemented further in other combinatorial scheduling problems. This case study considerably benefits the company by revising its time and minimizing its daily energy use. [ABSTRACT FROM AUTHOR]

The study here summarizes the work growth in "Particle Swarm Optimization (PSO)" since (1995 to 2021). Thirty-five papers have been analysed. Those research articles have been divided into various aspects in which PSO technique has been modified a lot during these years due to its simplicity. The hybridization of PSO with many evolutionary algorithms has been done during these years which helped us a lot to solve many optimizations and real-life problems. This paper discusses the progress, modification, improvement and application of PSO during these years. [ABSTRACT FROM AUTHOR]

This paper discusses a review structure in modified Maximum Power Point Tracking (MPPT) for a single phase Z-source inverter (ZSI) when connected to a photovoltaic (PV) module. Both soft computing MPPT mechanisms, Artificial Intelligence (AI) and Evolutionary Algorithm (EA) are very effective in dealing with nonlinear I-V characteristics of PV module. ZSI can be used to buck or to have boost operation at the inverter ac output voltage when the duty cycle is changed. In this paper, the selective MPPT control algorithms have been discussed in order to understand which controllers are the most effectively applied to maximize the power generation at the inverter output. At the end, this paper will tabulate a comparison between the AI and EA techniques which could reduce the complexity of implementation, tracking efficiency, control strategy and reliability towards ZSI. These tables will help in recognizing the suitable MPPT controllers that can be applied to a specific load for a new research exploration in the future. [ABSTRACT FROM AUTHOR]

The term optimization refers to find the best possible solution. Gradient computation and Nature evolution rule are the two main bases of almost all optimization algorithms. Single objective optimization problems deal with optimization of one objective function whereas multi-objective problems deal with two or more objective functions to be optimized. Many algorithms have been developed till now for the optimization of multi-objective problems. In this paper, we will review and compare two multi-objective evolutionary algorithms SPEA2 (Strength Pareto Evolutionary algorithm) and PAES (Pareto Archived Evolution strategy). Evolutionary algorithms use fitness function, selection, crossover, and mutation procedures to generate a new population. SPEA2 algorithm uses the population set as well as an external set known as an archive to select and generate the next population members. PAES uses a local search approach to generate new solutions and maintains an archive to refer to previously generated solutions and thereby find the dominance ranking of current and newly generated solutions. Four sets of problems (DTLZ1, DTLZ2, ZDT1, ZDT2) will be solved using these algorithms and the results of these algorithms on these test problems will be compared in this paper. [ABSTRACT FROM AUTHOR]

Energy load forecasting is a systematic method of predicting future loads in advance to maintain a balance between energy demand and supply. Sustainable energy development is critical in energy design, planning, generation, transmission, and distribution, which leads to computational complexities in conventional methods when it is non-linear. Bio-inspired optimization algorithms are part of artificial intelligence that is inspired by the biological behavior and evolutionary principles of nature. Due to advancements in artificial intelligence, an increasing number of heuristic optimization techniques have resulted in the efficient handling of complex problems. The complexities in the accurate prediction of electricity can be achieved with evolutionary algorithms and swarm intelligence optimization algorithms. It avoids unnecessary expenditure, overestimation, and underestimation of electric energy and maximizes capacity utilization. This paper reviews recent studies on population-based optimization techniques to handle complexities in energy load forecasting and smart energy management. [ABSTRACT FROM AUTHOR]

Inventory models for stock-dependent demand have been developed continuously in recent years. In this paper, we develop inventory dependent model for multi-items by considering stock-dependent demand and stock-dependent holding rate for a rack that has a specific capacity. Two models are developed. The first model is an individual order quantity for each item and the second model is a joint replenishment model. The non-linear model with constraint is solved using an evolutionary algorithm. A numerical example and sensitivity analysis are conducted to show how the models work and to get some management insights. The sensitivity analysis shows that there is no dominant model. However, the joint replenishment order outperforms the individual order when the rack capacity is tight and there is a high ordering cost [ABSTRACT FROM AUTHOR]

Evolutionary algorithms show outstanding performance when they are applied to optimization problems with a few variables, i.e. problems with less than a hundred continuous variables. Large-scale global optimization with continuous variables is still a challenging task for a wide range of evolutionary algorithms. Their performance decreases when the number of variables increases because the search space grows exponentially. Classic evolutionary algorithms cannot find a good solution using the allocated resources. A cooperative coevolution approach is a good tool for increasing the performance of an optimizer in solving high-dimensional problems. The approach splits the objective vector into a predefined number of parts (subcomponents), and each part is optimized by its optimizer. This paper makes an effort to solve the problem of selecting the number of subcomponents. The paper represents a novel metaheuristic for solving optimization problems with a huge number of continuous variables. The suggested approach is based on the self-adaptive cooperation of algorithms and the cooperative coevolution approach. Each algorithm has a unique number of subcomponents. The metaheuristic automatically allocates resources between algorithms during the optimization process. Algorithms optimize the same population one by one. The proposed metaheuristic is titled COSACC, coordination of self-adaptive cooperative coevolution algorithms. We have evaluated the proposed algorithm on fifteen problems from the IEEE LSGO CEC'2013 benchmark. The study demonstrates that COSACC outperforms in average cooperative coevolution algorithms with the static number of subcomponents. Wilcoxon test has proven the results of numerical experiments. We have tested COSACC performance with other state-of-the-art metaheuristics, COSACC is a competitive approach. [ABSTRACT FROM AUTHOR]

Load balanced cluster formation in IoT based wireless sensor networks being reported in recent research literature as a well studied NP-hard problem has no trivial or obvious solution. Several protocols proposed in recent studies to resolve the load balancing problem mostly resort to communication intensive centralized algorithms or computation intensive memetic or evolutionary algorithms, that pose significant challenge on their applicability in pragmatic network deployment in real application scenario. This paper presents a simple yet effective distributed greedy algorithm for Energy efficient load balanced cluster assignment (EELBCA) for IoT based large scale sensor network. Our algorithm works in completely localized manner without the need of global topological information about the network. With simple and need based limited message passing the nodes self organize themselves to form well balanced clustered network solely based on their neighborhood information without the need of any computational intensive procedure. We evaluated our algorithm for various deployments viz. Random, Grid based and Gaussian networks and found our work seamlessly works on all such deployments. Simulation results shows our algorithm not only significantly minimizes on the standard deviation of loads among different cluster heads by 20-40% but also minimizes the energy requirement by 70-90% as compared to well documented existing algorithms for different network scenario and different node distribution in the network. [ABSTRACT FROM AUTHOR]

Fault location observability is an essential procedure among smart monitoring and outage management system issues used to realize self-healing networks, one of the most important characteristics of modern power systems. Further, the determination of the location of a faulty transmission line in a power grid is a vital topic to facilitate the self-healing network and to maintain the continuity of power supply. The self-healing procedure is to constantly keep the energy supplies to the customers in case of a power system configuration change such as a fault event happening on an electrical power transmission network. Self-healing is an operational aspect of electrical power networks for real-time identification and localization of faulty transmission lines for the whole power network. The implementation of PMUs as an accurate measurement device is a promising key entry for fault location and clearing utilizations. This paper proposes algorithmic models that determine the optimal number of PMUs and their placement sites to achieve a fault observable system whereas the state-estimation issue is examined. The optimization models discussed herein find a configuration of PMU locations with a least number of devices that locates any fault occurring in a transmission power network. An optimization process is presented within a nonlinear programming model coupling a synchronized phasor as well as conventional measurements. As a comparative optimization tool, a zero-one integer constraint linear program is implemented that satisfies global optimality with sufficient precision. Evolutionary algorithms such as a binary-particle swarm optimization and genetic algorithms have been adopted to characterize a fault observable power system. The algorithmic models are illustrated with an IEEE-14 bus system. Simulation results are tested on different size power systems to verify the efficacy of the proposed algorithmic approach without taking into account the fault classification, pre-fault loading conditions or considering the fault resistance. We present that this wide-area measuring implementation and its numerical results is an outstanding approach that improves the state-of-the-art in measurement driven methods regarding the finding of an optimal PMU set solution to pinpoint a fault event occurring anywhere in the power transmission grid. As proved via the numerical optimization, the mathematical and evolutionary algorithms are efficient and robust in finding some new benchmark optimal results for faulty conditions occurring in a modern power grid for any IEEE power system and case study. [ABSTRACT FROM AUTHOR]

The paper considers a new approach to the identification of dynamic objects in the form of the first-order differential equations system. The peculiarities of the proposed approach are the absence of restrictions on the structure of the differential equations included in the system and the symbolic representation of the obtained solution. The proposed approach includes a self-configuring genetic programming algorithm for selecting the structure of differential equations. The number of algorithms corresponds to the number of equations in the system. The method of differential evolution is used to optimize numeric constants. The authors apply a self-configuring procedure for the indicated evolutionary algorithms. The proposed approach has been tested on a variety of problems. The accuracy of the obtained solution has been investigated depending on the presence of noise in the sample data. [ABSTRACT FROM AUTHOR]

Modern artificial neural network architectures and training algorithms are able to achieve high accuracy in a wide range of problems. However, training multilayer neural networks using backpropagation or evolutionary algorithms might take a large amount of time. Extreme learning machines (ELMs) are aimed to resolve this problem by excluding training from the neural network model creation process by randomly initializing weights between input and hidden layers and computing weights between hidden and output layers. However, random weights initialization might lead to suboptimal results produced by the network. In this paper, we apply biology-inspired algorithms, including genetic algorithm with tournament selection, particle swarm optimization, and chaotic fish school search with exponential step decay, to the selection of input weights in ELM, the obtained ELM configurations are applied to solve regression problems. The results of the study show, that population-based algorithms can improve ELM accuracy. [ABSTRACT FROM AUTHOR]

Forecasting the bank profile shape of stable hydraulic channels using empirical, experimental and numerical models is of special consideration among fluid mechanic and river science engineers. In the present paper, the application of soft computing methods is evaluated in predicting the geometry of stable channel cross sections. In this way, using a combination of the Particle Swarm Optimization (PSO) algorithm with an Adaptive Neuro-Fuzzy Inference System (ANFIS) model, a novel evolutionary system called ANFIS-PSO is presented. The evolutionary model performance is assessed in comparison with a simple ANFIS model. The coordinates of points located on a channel boundary in stable state were also measured by the authors using a sensor instrument in the laboratory at 4 different flow discharge rates of 1.157, 2.18, 2.57 and 6.2 l/s. The results indicate that the evolutionary ANFIS-PSO model with Root Mean Squared Error (RMSE) and Mean Absolute Relative Error (MARE) of 0.0132 and 0.1326 performed better than the ANFIS model with 0.026 and 0.1426 error values respectively (almost 97% and 10% decrease in RMSE and MARE value for the ANFIS-PSO model, respectively). This demonstrates the high ANFIS-PSO model accuracy in predicting bank profile characteristics. The robust evolutionary ANFIS-PSO proposed can be used in designing and estimating stable channel dimensions. The second-degree polynomial equation proposed by the evolutionary ANFIS-PSO model can be utilized in predicting the coordinates of other points located on a stable boundary of a channel cross section. [ABSTRACT FROM AUTHOR]

In energy management, providing accurate findings to anticipate electrical load consumption is critical. The goal of this study is to develop methods for predicting electrical load, including artificial intelligence, neural network, ARIMA models, Bayesian models, and regression models. This paper proposes a new support vector regression application with the chaotic algorithms for electrical load forecasting in the southern region (Basra, Maysan, Dhi Qar, Muthanna), When choosing three parameters of a model (SVR) using evolutionary algorithms, you often encounter problems of early convergence, slowly reaching the solution of global optimization or falling into local optimization, and to overcome early local optimization in determining three parameters of the model (SVR) a chaotic algorithm is used. The 1980-2019 electrical load was used as a data set, and the results (CGASVR) were compared with (CPSOSVR) and (CIASVR) to choose the best form of electrical load forecasting where results show that the CGASVR model is more superior and efficient based on MSE, MAE, MAPE and MPE. [ABSTRACT FROM AUTHOR]

The scheduling of power generating units is also recognized as Unit Commitment (UC); is an important and complex constrained mixed-integer nonlinear optimization problem. Traditionally UC was solved for thermal Units only, however, with the widespread adoption of Renewable energy sources like wind farms, UC is now being solved for hybrid generating units. This makes UC even more challenging due to the inherent uncertainty of the velocity of the wind. Evolutionary Algorithms (EAs) are used for solving UC as the traditional techniques employed for solving UC, like Dynamic Programming, suffer from the 'curse of dimensionality. However, EAs are unconstrained optimization techniques, and that is why they require special methods for handling constraints. Operator design and constraint handling in an EA is largely dependent on the problem representation. This paper proposes a novel adaptive quantum-inspired evolutionary algorithm with binary and real representation and a novel Repair heuristic for constraint handling in solving UC with thermal and wind farms. Initial testing has been performed on a well-known UC problem with ten thermal units and wind farm for twenty-four hours varying load model. The results achieved are competing as compared to other well-known techniques. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application mechanism of a novel evolutionary algorithm to evaluate the financial performance of a power system while dealing with a combined economic and emission dispatch problem posed by various technical constraints. In particular, as a constrained objective function is associated with weighting factor scenarios, this problem considers the two dispatches for fuel and environmental aspects. Running simulations indicate that minimum costs are determined by weighting factors applied to the problem as a whole. Reducing total fuel costs with a dispatching priority and a pollutant target based on emission production has presented different implications as far as its contribution to the financial performance. Moreover, the increasing load demand results in generated power, costs, and emission discharges linked to the parameters and unit commitments. [ABSTRACT FROM AUTHOR]

Recently assembly line balancing (ALB) is an ultimate focus issue in manufacturing industries for optimizing both common goals which were the number of workstations and the production rate. Metaheuristic is one of optimization problem-independent technique. This paper focusses on the short review of metaheuristic optimization in solving assembly line balancing problems. Hence, to produce a summary of the review, this paper selects some publications for the past 3 years from year 2016 to 2019. From the review shows the metaheuristic optimization that often used are Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Differential Evaluation (DE), Evolutionary Algorithm (EA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Besides, GA is most frequently used to solve ALB problems. Finally, recommendation for future research for solving ALB Problems using metaheuristic optimization are discussed. [ABSTRACT FROM AUTHOR]

The paper presents the mathematical modeling of the interstitial hyperthermia process caused by the action of internal applicator using electric field. As one knows, the increase of the tissue temperature causes the destruction of the cancer tissue. To estimate the degree of damage of the tumor tissue the Arrhenius scheme is used. From the point of view of the possibility of supporting thermotherapeutic techniques, it is important to examine the impact of individual parameters occurring in the mathematical model on the degree of tissue destruction. Therefore, it is necessary to estimate, based on the sensitivity analysis methods, which parameter has the greatest effect on the computations results. In the paper also the results of the inverse problem consisting in the simultaneous identification of the selected parameters controlling the process of tissue damage are also shown. The inverse problem is solved using the evolutionary algorithm. As a parameter of the fitness function for the evolutionary algorithm, the Arrhenius integral, which determines the degree of tissue destruction, is applied. The finite element method is used to solve the coupled problem related to the heating of biological tissues. [ABSTRACT FROM AUTHOR]

Increasing the customer demand causes an increase in the number of flights for the case study low-cost airline. According to the low-cost airline industry, the airline focuses on minimizing total cost. In addition, we also aim rosters satisfying operation are balance workload, the priority of seniority (bidline), and balance flight assignment. This paper attempts to solve individual cockpit crew pairing assignment to each flight pattern. In this paper, we propose the multi-objective evolutionary algorithm based on decomposition algorithm (MOEA/D) to compare with honey bees mating optimization (HBMO) for solving many objectives and complex cockpit crew scheduling problem. [ABSTRACT FROM AUTHOR]

When the robot travels on the surface of different media, the uncertainty of the medium will seriously affect the autonomous action of the robot. In this paper, the distribution characteristics of multiple electrostatic charges on the surface of materials are detected, so as to improve the accuracy of the existing electrostatic signal material identification methods, which is of great significance to help the robot optimize the control algorithm. In this paper, based on the electrostatic signal material identification method proposed by predecessors, the multi-channel detection circuit is used to obtain the electrostatic charge distribution at different positions of the material surface, the weights are introduced into the eigenvalue matrix, and the weight distribution is optimized by the evolutionary algorithm, which makes the eigenvalue matrix more accurately reflect the surface charge distribution characteristics of the material. The matrix is used as the input of the k-Nearest Neighbor (kNN)classification algorithm to classify the dielectric materials. The experimental results show that the proposed method can significantly improve the recognition rate of the existing electrostatic signal material recognition methods. [ABSTRACT FROM AUTHOR]

The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study. [ABSTRACT FROM AUTHOR]

The demand for electricity has increased With the increase in the number of electrical gadgets and appliances, it is now very important to emphasize in the concept of demand side management to meet the required demand of electricity. DSM assures efficient use of available energy resources. DSM along with smart grid allows the consumer to have details of power consumption information and allows the sup pliers to lower the overall load demand during the peak period and modify the load curve. Necessary data of the energy consumption and price have been taken from different papers. In this research work, the primary aim is to mainly lower the overall load demand and thus the operational cost and utility bills. The work is done in different areas – residential, commercial and industrial. To get the optimized result, Meta- heuristic algorithm was developed to minimize the problem. Because of the flexibility, gradient free mechanisms and local optima avoidance of the algorithms, meta-heuristic techniques are gaining popular. The evolutionary algorithms used to solve the demand side management problem are Particle Swarm Optimization, Firefly Algorithm and Salp Swarm Algorithm. The three algorithms are used to obtain the objective of the project work in three different areas- residential, commercial and industrial area. Firefly algorithm is found out to provide the best cost as compared to other two algorithms PSO and SSA. Firefly Algorithm is able to provide better accuracy. [ABSTRACT FROM AUTHOR]

A new evolutionary algorithm called colliding bodies optimization (CBO) considered in this paper to solve optimal power flow (OPF) problem in a power system network. CBO is based on natural phenomenon of one dimensional collision between two objectives. It does not have any control parameters and simple in structure. IEEE 30-bus system with various objective functions like, minimization of total fuel cost with valve point loading effect, minimization of emission profile are considered to test the feasibility and effectiveness of the CBO algorithm. In comparison with the other existed methods presented in the literature, the proposed CBO algorithm gives better optimal solutions for single objective OPF problems. [ABSTRACT FROM AUTHOR]

Clonal Selection Algorithm (CSA) is a sturdy evolutionary algorithm that mimics the biological immune system mechanism, frequently implemented as a searching technique in solving complex modelling tasks such as pattern recognition and combinatorial optimization. Subsequently, 3-Satisfiability logic mining is the fundamental medium to induce real data sets in a more precise manner. In this paper, a modified clonal selection algorithm with Hopfield neural network (HNN) is proposed for 3-Satisfiability data mining. Somatic hypermutation operator in CSA can reduce the iterations during HNN training phase and retrieve the best logical rule for a minimal testing error. In addition, 3- Satisfiability logical rule provides better attributes representation of the data set. The proposed method will be applied on Amazon's Employees Resources Access data set to predict the approval or denial for an unseen set of employees in the future. The performance evaluation of the proposed method will be compared with exhaustive search (ES) by computing plausible errors such as root mean square error (RMSE), mean absolute error (MAE), sum of squared error (SSE), accuracy and computational time. The computational simulations are carried out by manipulating different number of neurons to verify the capability of the proposed method in data mining. The experimental results have shown a better performance of the proposed model in training and retrieval phase of the Amazon's Employees Resources Access data set in comparison with ES used in this research. [ABSTRACT FROM AUTHOR]

Optimal design and operation of energy systems serving clusters of buildings interconnected by energy microgrids is a scientific challenge for the engineering community, with many interdisciplinary aspects involved. In the paper, the optimization problem is tackled in terms of simulation-based design of the energy system for a typical year of operation. The methodology has been applied to a Campus in Trieste, Italy, involving locally available renewable energy sources, a centralized cogeneration system, and decentralized heat pumps. The nominal powers of cogeneration plant, photovoltaic modules and wind turbine have been optimized by a population-based evolutionary optimization algorithm, previously proposed by the authors. We have also found the optimal scheduling of energy generators by means of a greedy approach. The solution with maximum Annualized Cost Saving Percentage is discussed, highlighting how a configuration involving decentralized heat pumps is cost effective, integrates more renewable energy sources, and reduces environmental impact and grid exchange compared to a benchmark solution, with fully centralized generators. [ABSTRACT FROM AUTHOR]

Logic programming is extensively used to describe relations and have both a declarative and an operational meaning. A 3-Satisfiability (3-SAT) program is a class of Boolean Satisfiability problem, where strictly 3 literals per clause are involved. There are various algorithms utilized to solve the 3-Satisfiability logic program. Exhaustive Search (ES) is the standard method, where the algorithm enumerates for every possible iteration to search for a solution. Imperialist Competitive (IC) algorithm, on the other hand, an evolutionary optimization method that is inspired by the imperialistic competition model. Applying the Imperialist Competitive algorithm to 3-Satisfiability logic programming shows its ability in dealing with different types of optimization problems. In this paper, the objective was to compare IC algorithm and ES algorithm in doing 3-SAT logic programming with Hopfield Neural Network (HNN). The performance of both models were evaluated by performance evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Global Minima Ratio and CPU Time. The expected outcome of the experiment is that the IC algorithm outperformed ES in 3-SAT logic programming. [ABSTRACT FROM AUTHOR]

In this paper we present an approach for Vehicle Routing Problem with Drones (VRPD) in case of building inspection from the air. In autonomic inspection process there is a need to determine of the optimal route for inspection drone. This is especially important issue because of the very limited flight time of modern multicopters. The method of determining solutions for Traveling Salesman Problem(TSP), described in this paper bases on Parallel Evolutionary Algorithm (ParEA) with cooperative and independent approach for communication between threads. This method described first by Bożejko and Wodecki [1] bases on the observation that if exists some number of elements on certain positions in a number of permutations which are local minima, then those elements will be in the same position in the optimal solution for TSP problem. Numerical experiments were made on BEM computational cluster with using MPI library. [ABSTRACT FROM AUTHOR]

For the system of multiple types of EOS (Earth Observing Satellites), it is a vital issue to assure that each type of payloads carried by the group of EOS can be used efficiently and reasonably for in astronautics fields. Currently, most of researches on configuration of satellite and payloads focus on the scheduling for launched satellites. However, the assignments of payloads for un-launched satellites are bit researched, which are the same crucial as the scheduling of tasks. Moreover, the current models of satellite resources scheduling lack of more general characteristics. Referring the idea about roles-based access control (RBAC) of information system, this paper brings forward a model based on rolemining of RBAC to improve the generality and foresight of the method of assignments of satellite-payload. By this way, the assignment of satellite-payload can be mapped onto the problem of role-mining. A novel method will be introduced, based on the idea of biclique-combination in graph theory and evolutionary algorithm in intelligence computing, to address the role-mining problem of satellite-payload assignments. The simulation experiments are performed to verify the novel method. Finally, the work of this paper is concluded. [ABSTRACT FROM AUTHOR]

The paper deals with the prediction of the structure of non-carbon nanotubes (N-NT), based on the USPEX software package. Compounds of the type AX2, in particular WS2 are considered. Results of calculations using the evolutionary algorithm USPEX are presented. The external computer programm MOPAC (designed to implement semi-empirical quantum chemistry algorithms) interfaced with USPEX was used for the relaxation of structures. [ABSTRACT FROM AUTHOR]

This paper explores a computational intelligence based on a natural phenomenon of thunderstorm mechanisms. These works are applied to optimize a technical problem under various operational constraints and environmental requirements considered the IEEE-62 bus system. Obtained results show that Thunderstorm Algorithm demonstrates faster convergence and short time. This method also presents smooth and stable characteristics. In detail, the optimal solution is obtained in 22 steps for 15,586 $/h after pointing at 28,858 $/h at the first streaming. Based on executions, the computation needed 2.5 s for the streaming and it also needed 0.09 for covering the dead tracks included 0.5 s for the replacement. [ABSTRACT FROM AUTHOR]

This research deals with the comparison of the two modern approaches for evolutionary algorithms, which are the adaptivity and complex chaotic dynamics. This paper aims on the investigations on the chaos-driven Differential Evolution (DE) concept. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of chaotic pseudo random number generators for the DE and comparing the influence to the performance with the state of the art adaptive representative jDE. This research is focused mainly on the possible disadvantages and advantages of both compared approaches. Repeated simulations for Lozi map driving chaotic systems were performed on the simple benchmark functions set, which are more close to the real optimization problems. Obtained results are compared with the canonical not-chaotic and not adaptive DE. Results show that with used simple test functions, the performance of ChaosDE is better in the most cases than jDE and Canonical DE, furthermore due to the unique sequencing in CPRNG given by the hidden chaotic dynamics, thus better and faster selection of unique individuals from population, ChaosDE is faster. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) is an area of wireless technologies that is attracting a great deal of interest. There are still several areas of VANETS, such as security and routing protocols, medium access control, that lack large amounts of research. There is also a lack of freely available simulators that can quickly and accurately simulate VANETs. The main goal of this paper is to develop a freely available VANETS simulator and to evaluate popular mobile ad-hoc network routing protocols in several VANETS scenarios. The VANETS simulator consisted of a network simulator, traffic (mobility simulator) and used a client-server application to keep the two simulators in sync. The VANETS simulator also models buildings to create a more realistic wireless network environment. Ad-Hoc Distance Vector routing (AODV), Dynamic Source Routing (DSR) and Dynamic MANET On-demand (DYMO) were initially simulated in a city, country, and highway environment to provide an overall evaluation. [ABSTRACT FROM AUTHOR]

The power user electric energy data acquire system (PUEEDAS) is an important part of smart grid. This paper builds a multi-objective optimization model for the performance of the PUEEADS from the point of view of the combination of the comprehensive benefits and cost. Meanwhile, the Chebyshev decomposition approach is used to decompose the multi-objective optimization problem. We design a MOEA/D evolutionary algorithm to solve the problem By analyzing the Pareto optimal solution set of multi-objective optimization problem and comparing it with the monitoring value to grasp the direction of optimizing the performance of the PUEEDAS. Finally, an example is designed for specific analysis. [ABSTRACT FROM AUTHOR]

In this paper, random closed sets (RCS) in Euclidean space are considered along with their distributions and approximation. Distributions of RCS may be used for the calculation of expectation and other characteristics. Reachable sets on initial data and some ways of their approximate evolutionary description are investigated for stochastic differential equations (SDE) with initial state in some RCS. Markov property of random reachable sets is proved in the space of closed sets. For approximate calculus, the initial RCS is replaced by a finite set on the integer multidimensional grid and the multistage Markov chain is substituted for SDE. The Markov chain is constructed by methods of SDE numerical integration. Some examples are also given. [ABSTRACT FROM AUTHOR]

Malaysia is one of the major producer countries for fishery production due to its location in the equatorial environment. Grouper fish is one of the potential markets in contributing to the income of the country due to its desirable taste, high demand and high price. However, the demand of grouper fish is still insufficient from the wild catch. Therefore, there is a need to farm grouper fish to cater to the market demand. In order to farm grouper fish, there is a need to have prior knowledge of the proper nutrients needed because there is no exact data available. Therefore, in this study, primary data and secondary data are collected even though there is a limitation of related papers and 30 samples are investigated by using standard deviation selection in Evolutionary algorithm. Thus, this study would unlock frontiers for an extensive research in respect of grouper fish feed formulation. Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable. The feasible and low fitness, quick solution can be obtained. These fitness can be further predicted to minimize cost in farming grouper fish. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is used for curve fitting by cubic Ball curves. DE is evolutionary algorithm that encodes solutions as vectors and applies some operations and exchange of components to generate new solutions from the existing ones. In this paper, the DE optimize the two control points in cubic Ball interpolant. In order to obtain the segment or data points, the boundary data of the images should be extracted and detected the corners. Hence, the images reconstruction conducted from the cubic Ball curve and approximated the original image. Some results and numerical examples illustrated in this study. [ABSTRACT FROM AUTHOR]

In this paper is reported our progress in the synthesis of two partially different areas of research: complex networks and evolutionary computation. Ideas and results reported and mentioned here are based on our previous results and experiments. The main core of our participation is an evolutionary algorithm performance improvement by means of complex network use. Complex network is related to the evolutionary dynamics and reflect it. We report here our latest results as well as propositions on further research that is in process in our group (http://navy.cs.vsb.cz/). Only the main ideas and results are reported here, for more details it is recommended to read related literature of our previous research and results. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem. [ABSTRACT FROM AUTHOR]

A fuzzy evolutionary algorithm is an integration of an evolutionary algorithm and a fuzzy system. In this paper, we present an application of a genetic algorithm to a fuzzy evolutionary algorithm to detect and to solve chromosomes conflict. A chromosome conflict is identified by existence of any two genes in a chromosome that has the same values as two genes in another chromosome. Based on this approach, we construct an algorithm to solve a lecture scheduling problem. Time codes, lecture codes, lecturer codes, and room codes are defined as genes. They are collected to become chromosomes. As a result, the conflicted schedule turns into chromosomes conflict. Built in the Delphi program, results show that the conflicted lecture schedule problem is solvable by this algorithm. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. In this paper we have performedan experimental studythat indicates the use of genetic algorithms for the two dimensional bin packing problem. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. We propose a new solution of the problem by a genetic algorithm. [ABSTRACT FROM AUTHOR]

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window and fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection. [ABSTRACT FROM AUTHOR]

Over the years, nurse scheduling is a noticeable problem that is affected by the global nurse turnover crisis. The more nurses are unsatisfied with their working environment the more severe the condition or implication they tend to leave. Therefore, the current undesirable work schedule is partly due to that working condition. Basically, there is a lack of complimentary requirement between the head nurse's liability and the nurses' need. In particular, subject to highly nurse preferences issue, the sophisticated challenge of doing nurse scheduling is failure to stimulate tolerance behavior between both parties during shifts assignment in real working scenarios. Inevitably, the flexibility in shifts assignment is hard to achieve for the sake of satisfying nurse diverse requests with upholding imperative nurse ward coverage. Hence, Evolutionary Algorithm (EA) is proposed to cater for this complexity in a nurse scheduling problem (NSP). The restriction of EA is discussed and thus, enhancement on the EA operators is suggested so that the EA would have the characteristic of a flexible search. This paper consists of three types of constraints which are the hard, semi-hard and soft constraints that can be handled by the EA with enhanced parent selection and specialized mutation operators. These operators and EA as a whole contribute to the efficiency of constraint handling, fitness computation as well as flexibility in the search, which correspond to the employment of exploration and exploitation principles. [ABSTRACT FROM AUTHOR]

Most researchers have doubts over what type of classification model is most suitable for representing their data when applying a data mining approach. However, this quandary can be solved by performing a comparison between the available models. Generating multiple classification models simultaneously is cost-effective when using free data mining tools such as KEEL (Knowledge Extraction based on Evolutionary Learning). KEEL is an interactive data mining software that provides a high level of functionality for users. This paper presents an experience of using this user-friendly software to solve a fatigue data segment classification problem. The problem concerns classifying fatigue data segments whether they consist of low or high damage. Experiments over the dataset using three different types of classification algorithms (i.e. CN2, C4.5 and Naïve-Bayes), two discretization methods, and two filter-type feature selection methods show that there is no optimal classification algorithm in terms of indexing performance for our specific problem. Interestingly, however, this study does provide minor evidence that classifier performance could be increased by pairing discretization and feature selection methods appropriately. [ABSTRACT FROM AUTHOR]