Search

The travelling salesman problem (TSP) and its variants have been studied extensively due to its wide range of real-world applications, yet there are challenges in providing efficient algorithms to deal with some of its variants. The multiple travelling salesman problem (MTSP), is the generalization of TSP, which aims to determine m − routes for ' m ' salesmen to cover a set of n − cities exactly once where each route starts and ends at a depot such that the total distance is least. In this, the number of cities in each route of the optimal solution may be distributed disproportionately. This paper presents, a bi-objective MTSP (BMTSP) with the load balancing constraint, where the first objective is to minimize the total travel distance and the second objective minimizes the total time. A metaheuristic based genetic algorithm with tournament selection (GATS) is designed by integrating with mixed strategies, such as flip, swap and scramble in mutation operation to obtain efficient Pareto solution for BMTSP. The computational experiments are carried out on different data sets, which are derived from the TSPLIB. The performance of GATS is compared with different genetic approaches and simulation results show that the proposed GATS obtained improved solutions on some of the benchmark instances. [ABSTRACT FROM AUTHOR]

This paper presents the development of a web-based course timetabling system based on an enhanced genetic algorithm. The enhanced method utilizes a heuristic mutation which concentrates on mutating the infeasible genes to improve the algorithms' exploration and exploitation capability. The method was implemented using a free and open-source application and can be accessed online. Based on the actual datasets from Caraga State University, the enhanced method optimized the use of classroom resources by using a smaller number of rooms. The generated timetable is more efficient as it satisfies not just hard constraints, which are conflicting schedules, but also soft constraints. [ABSTRACT FROM AUTHOR]

This paper proposes the application of control by state feedback using the linear quadratic regulator (LQR) optimized by metaheuristics to damp low-frequency electromechanical oscillations in electrical power systems. The current sensitivity model was used to represent the single machine infinite bus (SMIB) system in the time domain. The weighting matrices of the LQR were adjusted using four different algorithms: (i) the genetic algorithm, (ii) the differential evolution algorithm, (iii) the particle swarm optimization algorithm, and (iv) the gray wolf optimization (GWO) algorithm. In the cases considered, disturbances were applied to the electrical power system and, then, performances comparisons associated with each metaheuristic were statistically analyzed, in which the number of iterations, error, and time to achieve convergence of each algorithm were compared. From the results, it was possible to conclude that the algorithms were efficient in adjusting the weighting matrices of the LQR, providing additional damping to the poles of interest of the system. It was also possible to conclude that the GWO algorithm presented the best performance, accrediting it as a powerful tool in the study of small-signal stability for the analyzed case. • Design of an LQR for small-signal stability analysis of power systems. • Implementation metaheuristics for adjusting the parameters of the LQR. • Comparison of the performance of evolutionary and swarm-based algorithms. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new application of WDE for estimation of nonlinear Hammerstein-Wiener systems. • Designed WDE is implemented for measured input, internal and output data viably. • The sundry scenarios of HWM establish robustness, stability and convergence of WDE. • Statistical observations prove efficacy for controlled autoregressive scenarios. Knacks of evolutionary computing paradigm-based heuristics has been exploited exhaustively for system modeling and parameter estimation of complex nonlinear systems due to their legacy of reliable convergence, accurate performance, simple conceptual design ease implementation ease and wider applicability. The aim of the presented study is to investigate in evolutionary heuristics of weighted differential evolution (WDE) to estimate the parameters of Hammerstein-Wiener model (HWM) along with comparative evaluation from state-of-the-art counterparts. The objective function of the HWM for controlled autoregressive systems is efficaciously formulated by approximating error in mean square sense by computing difference between true and estimated parameters. The adjustable parameters of HWM are estimated through heuristics of WDE and genetic algorithms (GAs) for different degrees of freedom and noise levels for exhaustive, comprehensive, and robust analysis on multiple autonomous trials. Comparison through sufficient large number of graphical and numerical illustrations of outcomes for single and multiple execution of WDE and GAs through different performance measuring metrics of precision, convergence and complexity proves the worth and value of the designed WDE algorithm. Statistical assessment studies further prove the efficacy of the proposed scheme. Extensive simulation based experimentations on measure of central tendency and variance authenticate the effectiveness of the designed methodology WDE as precise, efficient, stable, and robust computing platform for system identification of HWM for controlled autoregressive scenarios. [ABSTRACT FROM AUTHOR]

This paper is concerned with a variant of the traveling salesman problem (TSP) called the maximum scatter traveling salesman problem (MSTSP). The goal of MSTSP is to find a Hamiltonian cycle that maximizes the minimum length edge of the cycle. This problem has real-world applications in domains such as manufacturing and medical imaging. Both symmetric and asymmetric versions of the problem are considered in this paper. To address this problem, we have developed two evolutionary approaches. Our first approach is based on a genetic algorithm, whereas the second approach is based on a differential evolution algorithm. Initial solution generation procedure, variation operators (crossover and mutation) used in our approaches are designed considering the characteristics of this problem. The solutions obtained through variation operators are improved further through a local search that is specially adapted for MSTSP. For benchmarking, we have compared the performance of our proposed approaches with the state-of-the-art approaches available in the literature. Our approaches obtained better quality solutions in a shorter time for both symmetric and asymmetric versions of the problem, thereby clearly demonstrating their effectiveness in solving MSTSP. • This paper is concerned with the maximum scatter traveling salesman problem (MSTSP). • Two evolutionary approaches are proposed for solving this problem. • First approach is a steady-state genetic algorithm, while the latter is a discrete differential evolution algorithm. • Various components of our approaches are designed considering the characteristics of MSTSP. • Computational results show the effectiveness of our proposed approaches. [ABSTRACT FROM AUTHOR]

During the past two decades, evolutionary algorithms have seen great achievements in solving complex optimization problems owing to the advantages brought by their properties, especially constrained multi-objective optimization problems (CMOPs) with multiple conflicting objective functions and constraints which widely exist in industry, scientific research, and daily life. Among the real-world CMOPs, mechanical design problems (MDPs) from the industry widely exist and are important, while unfortunately, most constrained multi-objective evolutionary algorithms (CMOEAs), developed based on benchmark CMOPs, neglect the specific features and challenges of MDPs and thus cannot solve them well to provide the practitioners promising Pareto optimal solutions for decision making. To overcome this limitation, this paper analyzes the features and challenges of MDPs, including badly scaled objective space, decision space properties, and decision variable linkages. Then, we propose a new CMOEA named CMORWMDP. First, instead of the homogeneous operator in existing CMOEAs, a heterogeneous operator strategy is adopted to use the operator of Genetic Algorithm to enhance the convergence and the operator of Differential Evolution to tackle variable linkages. In addition, an improved fitness function that considers normalization is designed for environmental and mating selections. The proposed algorithm is simple, parameter-free, and easy to implement. Experiments on 21 real-world MDPs show its superiority compared to 20 state-of-the-art CMOEAs under the Friedman test and Wilcoxon test on different metrics, demonstrating the effectiveness of the heterogeneous operator and normalization-based fitness for selections for real-world MDPs. Moreover, the effectiveness of the proposed algorithm in solving other real-world CMOPs is also verified, revealing that our methods are very promising in tackling real-world problems. • Challenges of real-world constrained multi-objective mechanical design problems. • Heterogeneous operator strategy to tackle the challenges. • Improved fitness function with normalization. • A parameter-free constrained multi-objective optimization evolutionary algorithm. [ABSTRACT FROM AUTHOR]

Reliability and maintainability are the key system effectiveness measures in process and manufacturing industries, and treatment plants, especially in E-waste management plants. The present work is proposed with a motto to develop a stochastic framework for the e-waste management plant to optimize its availability integrated with reliability, availability, maintainability, and dependability (RAMD) measures and Markovian analysis to estimate the steady-state availability of the E-waste management plant. In the analysis an effort is also made to identify the best performing algorithm for availability optimization of the e-waste plant. A stochastic model for a particular plant is developed and its availability is optimized using various metaheuristic approaches like a genetic algorithm (GA), particle swarm optimization (PSO), and differential evolutions (DE). The most sensitive component is identified using RAMD methodology while the effect of deviation in various failure and repair rates are observed by the proposed model. The failure and repair rates follow an exponential distribution. All time-dependent random variables are statistically independent. A novel stochastic model is presented for an e-waste management plant and optimum availability is obtained using metaheuristic approaches. The proposed methodology is not so far discussed in the reliability analysis of process industries. The numerical results of the proposed model compared to identify the most efficient algorithm. It is observed that genetic algorithm provides the maximum value (0.92330969) of availability at a population size 2500 after 500 iterations. PSO algorithm attained the maximum value (0.99996744) of availability just after 50 iterations and 100 population size. So, its rate of convergence is faster than GA. The optimum value of availability is 0.99997 using differential evolution after 500 iterations and population size of more than 1000. These findings are very beneficial for system designers. The proposed methodology can be utilized to find the reliability measures of other process industries. [ABSTRACT FROM AUTHOR]

• Introducing SMDM for accurate dynamic landscape change detection. • Empirical comparison of SMDM with cutting-edge methods in change detection. • Incorporating SMDM into the HMRS framework to boost dynamic optimization. • Comparing proposed method to advanced methods in dynamic e-procurement. The increasing dynamism of global markets, coupled with the occurrence of unpredictable events, has introduced substantial challenges in formulating efficient supply chain strategies. The inherent dynamic nature of logistic networks necessitates a departure from traditional supply chain methodologies. This study proposes an advanced solution for dynamic e-procurement utilizing evolutionary algorithms (EAs). In conventional supply chains involving buyers and suppliers, a critical challenge is identifying cost-efficient suppliers capable of fulfilling consumer demands amidst fluctuating prices and quantities. Traditional optimization techniques often fail to perform effectively under these dynamic conditions. Moreover, detecting changes during the optimization process is an additional hurdle in dynamic optimization problems. Recent advancements have demonstrated the efficacy of EAs in solving a variety of real-world dynamic optimization issues. This research introduces a novel evolutionary algorithmic framework that integrates the Hybrid Multipopulational Reinitialization Strategy (HMRS), with a proposed hybrid change detection mechanism (named Smirnov-based Multi-sensor Detection Mechanism (SMDM)) to address the dynamic e-procurement problems. The proposed framework enhances the algorithm's adaptability and responsiveness to real-time changes within the e-procurement environment. By effectively detecting and responding to these variations, the framework aims to optimize procurement processes, ensuring efficiency and robustness in managing fluctuating requirements and conditions inherent to dynamic e-procurement scenarios. The empirical analysis presented underscores the superiority of Differential Evolution (DE) variants over Genetic Algorithm (GA) variants within the procurement context. The detailed empirical study validates the effectiveness of the proposed dynamic approach in addressing the challenges associated with dynamic e-procurement. Considering real-world parameter fluctuations, the proposed approach demonstrates significant resilience, positioning it as a robust and efficient solution for optimizing the e-procurement process and adeptly managing the complexities of the supply chain environment. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has been a vital element of smart manufacturing. The high energy intensity or environmental sustainability issue of AM, however, has posed a great challenge to the future massive application, particularly laser-based direct energy deposition (L-DED). This study aims to determine the optimal processing parameters for energy-saving without compromising the geometrical appearance. A quantification model for energy efficiency at the process level was established, with two energy efficiency indicators of AM process. Then, a meta-heuristic Mayfly algorithm, augmented with Bayesian technique and mutation strategies, was proposed to improve hyperparameters in a typical machine learning model (XGBoost). Based on the full factorial L-DED experiments, this study compared the improved XGBoost with four types of XGBoost derivatives via four algorithm evaluation metrics. Non-dominated sorting genetic algorithm II was adopted to optimize the processing parameters subject to the constraints of geometrical appearance. Results indicated that the proposed algorithm outperformed other XGBoost derivatives in terms of prediction accuracy and convergence rate. The energy efficiency could be improved by 76.35 J/g or 6.78 % on average while ensuring the geometry of the deposited layers. This study could help enhance energy-efficient additive manufacturing via proper processing parameters selection and facilitate the sustainability in AM domain. [ABSTRACT FROM AUTHOR]

• In this work, we introduced the negative linear (NL) hypothesis as the criterion to optimize the feedback-based optimization approaches, which reveals the key to improving the parameters to their optima lies in acquiring maximum initial convergence speed and maintaining a negative linear decline thereafter. • Based on the hypothesis, an efficient genetic algorithm based on searching-strategy optimization (GA-SSO), which achieved a remarkable convergence speed 3.5 times faster and a final enhancement increase of 21.6 % compared to the regular GA, was proposed to accelerate the convergence speed. • The proposed hypothesis sheds light on the essence of parameter optimization and provides an effective way to expedite the optimization process for feedback-based wavefront shaping techniques. We believe that if program GA-SSO on an advanced FPGA processor chip, focusing or imaging through a dynamic medium with sub-second is feasible. Feedback-based wavefront shaping techniques are efficient approaches to compensate for multiple scattering and achieve desired focuses inside or through turbid media. In this paper, we introduced the negative linear (NL) hypothesis as the criterion to optimize the feedback-based optimization approaches, such as genetic algorithm (GA). This hypothesis reveals that the key to improving the parameters to their optima lies in acquiring maximum initial convergence speed and maintaining a negative linear decline thereafter. Based on the hypothesis, an efficient genetic algorithm based on searching-strategy optimization (GA-SSO), which applies dual adaptive control of mutation and fixed linear fitness scaling selection, was proposed to accelerate the convergence speed. The simulations and experiments confirmed that NL is a basic guide to optimize the parameters to the optima of feedback-based wavefront shaping techniques. [ABSTRACT FROM AUTHOR]

When the evolutionary algorithm is applied to handle high-dimensional expensive multi-objective optimization problems (MOPs), population evolution is crucial since it controls exploration and exploitation and decides if promising candidate solutions could be generated. However, little attention has been paid to this issue, evolution operators based on Genetic Algorithm (GA) and Differential Evolution (DE) are still the two most common approaches, whose convergence on high-dimensional MOPs with a limited number of fitness evaluations remains challenging. In this paper, we propose a decomposition-based local learning strategy to accelerate convergence in the high-dimensional search space of MOPs. Specifically, an individual is updated by learning from one of the best solutions of its corresponding local area based on the multi-objective decomposition approach. Accordingly, a surrogate-assisted evolutionary algorithm is proposed for better solving expensive high-dimensional MOPs. Experimental studies on MOPs with up to 100 decision variables and with 300 fitness evaluations demonstrate the effectiveness of the proposed method. Furthermore, we use the proposed method to solve a 2-objective shape design problem of the blended wing-body underwater glider (BWBUG) with 39 decision variables, and an impressive solution set is obtained. • Efficacy of evolution operators on high-dimensional EMOPs is analyzed. • Propose a novel evolution strategy based on decomposition for high-dimensional EMOPs. • A surrogate-assisted evolutionary algorithm is accordingly developed. • Comprehensive experimental studies on benchmark problems and an engineering application. [ABSTRACT FROM AUTHOR]

Population-based optimization algorithms are popular tools for optimization, influenced by two interactive factors: landscape bias, which guides the population toward better objective function values, and structural bias, which directs the population to a specific location in the search domain. Structural bias can impede the exploration of algorithms, compelling the population to revisit a particular area of the search domain, which leads to increased computing costs without yielding any new information. In this study, we present a comprehensive survey of structural bias. Identifying this bias in a general algorithm is challenging. We propose the 'Generalized Signature Test' as a methodology to detect and analyze the structural bias in population-based optimization algorithms. Numerical simulations are performed using six well-known metaheuristic algorithms: Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, and Harris Hawks Optimization. Experimental results suggest that Differential Evolution displays the least bias toward the center of the search domain, whereas the Harris Hawks Optimization exhibits a significant axial-diagonal bias toward the origin. Our findings indicate that the proposed method is both efficient and straightforward for assessing algorithmic structural bias. The Generalized Signature Test provides an in-depth analysis and understanding of biases, tailor-made for thorough bias behavior investigations. The Generalized Signature Test can identify bias in established algorithms and evaluate bias in emerging algorithms at their early development stages. [ABSTRACT FROM AUTHOR]

Airspace surveillance is a significant issue for many countries to control and manage their airspace. The number of radars used and their coverage rate are the main issues to consider in this case. Therefore, this paper addresses the problem of finding the best radar locations to obtain the highest coverage rate with the least possible number of radars in a certain region. The radar placement problem is considered as a multi-objective optimization problem with two objectives: the number of radars and the coverage rate. To perfectly solve this optimization problem, a set of multi-objective meta-heuristic approaches based on simulated annealing, memory-based steady-state genetic algorithm, a decomposition-based multi-objective algorithm with differential evolution, and non-dominated sorting genetic algorithm (NSGA-II) are utilized. Algorithms are tested on a dataset created using DTED-1 map elevation data for two different selected regions. Based on the results, the NSGA-II algorithm achieves the best results and the highest coverage ratios among the tested algorithms. Two improved versions of the NSGA-II algorithm are also proposed to enhance its performance and make it more suitable for solving this optimization problem. The experimental results show that a coverage rate of 98% could be achieved with a small number of radars, and by increasing the number of radars, it exceeds 99%. • This paper solves the radar placement problem using a set of metaheuristic approaches. • The problem is considered as a multi-objective optimization problem. • Two adaptive and enhanced versions of the NSGA-II algorithm are proposed. • The results show that a coverage rate of 98% can be achieved with just a few radars. [ABSTRACT FROM AUTHOR]

Many metaheuristic methods have been proposed and have shown promising performance in solving multi-objective optimization problems (MOPs). As a representative example, the competitive swarm optimizer (CSO) exhibits excellent performance in solving large-scale MOPs. However, CSO performs poorly when used straightforwardly for constrained MOPs with complex constraints or objective spaces. In this paper, we propose a dual-swarm-based CSO framework that co-evolves two swarms with different orientations: (i) CSO is improved using genetic algorithms, namely CSO-GA to form a convergence-directed swarm without considering constraints to accelerate the convergence. (ii) CSO is further improved by differential evolution, namely CSO-DE to form a search-directed swarm by considering constraints to enhance diversity and improving the search for the constrained Pareto front. Moreover, a new constraint relaxation method is proposed that combines the particle swarm feasible rate and current evolutionary generation to dynamically adjust the factor for decreasing or increasing constraint relaxation. Extensive experiments with 37 benchmark instances and nineteen real-world constrained MOPs demonstrate that our proposed algorithm yields significantly better or at least more competitive results than ten state-of-the-art methods. • A dual-swarm-based CSO framework is proposed for CMOPs. • Two swarms co-evolve to form different orientations. • A new constraint relaxation method is presented. • Results demonstrate the superiority of our approach. [ABSTRACT FROM AUTHOR]

• A model was created to examine the reliability of maintenance budget allocation. • The model was applied to network-level pavement maintenance in Canada. • The impacts of algorithm selection and parameter tuning were studied. • The significance of pavement clustering in increasing reliability was highlighted. • The importance of incorporating actual pavement improvement curves was clarified. The purpose of this paper is to increase the reliability of the network-level pavement maintenance budget allocation by reducing uncertainties of algorithm selection and parameter tuning. In this paper, reliability is defined as the ability of a network-level pavement maintenance plan to improve the condition of a pavement network within a certain budget. In order to quantify reliability, the reliability index is defined as the ratio between the post-maintenance network condition and the net maintenance cost. With this purpose in mind, a two-objective optimization model was developed. To test its applicability, the model was applied to a pavement network in Canada. The model was solved using the Non-Dominated Sorting Genetic Algorithm II and the differential evolution algorithms. Finally, the reliability indices of algorithms and termination criteria were computed. The results indicated that the differential evolution algorithm recommended less frequent but more intense interventions that made the solutions expensive and less reliable. This paper contributed to the network-level pavement maintenance body of knowledge by (1) developing a multi-objective optimization model to reduce uncertainties of parameter tuning and algorithm selection; (2) increasing the reliability of budget allocation; (3) showcasing the significance of pavement clustering in increasing reliability; and (4) developing and incorporating actual pavement improvement curves. [Display omitted] [ABSTRACT FROM AUTHOR]

Since last few years, microarray technology has got tremendous application in many bio-medical researches. However, in order to efficiently recognize and apply this technology into the bio-medical areas is still very difficult and expensive. There are many metaheuristic approaches has been developed with different biological interpretation. Despite the existence of several approaches, there is always a requirement of development of more robust and efficient approach. In this work a new metaheuristic approach is proposed implementing binary shuffled frog leaping algorithm (BSFLA) for gene selection. To obtain an optimal gene subset, 20 different combination of gene subset is extracted from the original dataset. Out of which the optimal gene subset is identified implementing KNN classifier. Superiority of these gene set is shown using few other classifiers such as ANN and SVM. The model performance is also compared with few other metaheuristic approaches such as particle swarm optimization, differential evolution and genetic algorithm. [ABSTRACT FROM AUTHOR]

Before the terms "smart city" and "floating city" were introduced, the world's population had increased and land shortage across the world was already widely recognized. As a first challenge, the previous studies have developed the concept of a smart city as a creative answer, following that, several scientists proposed the floating city concept in the literature as a solution to the increased sea levels. Moreover, engineers, architects, and designers deal with city planning, for smart and floating settlements as a difficult design challenge, and evolutionary algorithms could be employed to address this complex problem by optimizing residents' needs. As a continuation of our previous studies on this topic, this time, we develop a multi-objective continuous genetic algorithm with differential evolution (DE) mutation strategy (MO_CGADE) and a multi-objective ensemble differential evolution algorithm (MO_EDE) to solve the problem on hand. Then, we compare the performance of the MO_CGADE and MO_EDE algorithms with the non-dominated sorting genetic algorithm (NSGAII) to maximize two conflicted objective functions, namely, scenery, and walkability in the proposed smart floating city model created in the Grasshopper Algorithmic Modeling Environment. The parametric model that we create in the Grasshopper software includes 64 decision variables, area constraints and objective functions to be optimized by MO_CGADE, MO_EDE, and NSGAII algorithms. Computational results show that MO_CGADE and MO_EDE algorithms generate better Pareto ranking results than the traditional NSGAII algorithm in terms of cardinality, distribution spacing, and coverage metrics. [ABSTRACT FROM AUTHOR]

A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general framework to solve problems with diverse characteristics. In this work, five optimisation algorithms are included in the ensemble: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Covariance Matrix Adaption-Evolution Strategy (CMA-ES), Differential Evolution (DE), and Modified Cuckoo Search (MCS). A geometric path-finding problem with numerous local minima is used to demonstrate the advantage of SPO. The effectiveness of the approach is compared with that of stand-alone incidences of the integrated optimisation strategies and with state-of-the-art algorithms. In addition, a benchmark test suit composed of engineering applications is utilised to validate the general applicability of SPO with respect to a variety of problems. The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally. • A supervised parallel optimisation (SPO) ensembles distinct optimisers. • SPO balances exploration/exploitation to solve diverse problems. • Extensive tests based on SPO ensemble using optimisers PSO, GA, CMAES, DE, MCS. • SPO outperforms isolated algorithms finding reproducible solutions. • The strategy reduces the necessity of exhaustive hyperparameter fine-tuning. [ABSTRACT FROM AUTHOR]

Offshore large-scale cluster extended reach wells (ERWs) are widely used to develop offshore oil & gas resources. Due to the complex downhole environments and complicated geological conditions, drilling parameters real-time optimization is challenging in offshore large-scale cluster ERWs drilling. In this paper, a real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on an intelligent optimization algorithm and machine learning (IOA-ML) is proposed. The method takes ROP as the objective function, establishes a ROP model based on long short-term memory (LSTM) neurons, and obtains drilling parameters optimization results asynchronously by combining the genetic algorithm, differential evolution algorithm, and particle swarm algorithm. The results show that ROP has been improved by 33.33% on average after the optimization by this real-time intelligent optimization method with an optimization time within 60s, which meets the requirements of real-time optimization of drilling parameters. • A real-time drilling parameters optimization method for ERWs drilling is proposed. • Parameters of offshore large-scale cluster ERWs drilling are optimized based on IOA-ML. • The method meets the requirements of real-time optimization of drilling parameters. [ABSTRACT FROM AUTHOR]

Electric power system applications are intricate optimization problems. Most literature reviews focus on studying an electrical paradigm through different optimization techniques. Since no review targets Metaheuristics (MHs) in electric power system applications, our work provides a general panorama of the paradigms that underlay such applications: Renewable Energies, Load Forecasting, Power Flow, Microgrids and Smart grids, and Power Quality. Our analysis revealed that the most employed MHs are Particle Swarm Optimization, Gray Wolf Optimizer, Genetic Algorithms, Cuckoo Search, and Differential Evolution. Historically, MHs have been classified into metaphor-based and non-metaphor-based. However, in some cases, this categorization does not correspond to pure MH procedures. Therefore, we also analyze MHs from a more formal perspective: their search operators. Moreover, we detected that the Renewable Energies paradigm presents a strong synergy with the remaining ones. Plus, there is a significant interest in topics related to Load-Forecasting optimization problems. Based on these data, we provide helpful recommendations for the current challenges and potential research paths. In doing so, our insights can support researchers and practitioners interested in furthering this field. • Systematic review of metaheuristics in electric power systems optimization. • Detailed taxonomy of optimization problems in electric power systems. • Identification of dominant metaheuristics for electric power system paradigms. • In-depth analysis of predominant metaheuristics from their search operators. • Discussion on challenges and opportunities in electric power systems optimization. [ABSTRACT FROM AUTHOR]

• Proposed an alternative method for big data optimization problems. • A hybrid between salp swarm algorithm and differential evolution is used as multi-objective for big data optimization. • A set of single objective and multi-objective problems from the 2015 big data optimization competition is used. • Experimental results show that the proposed method outperforms than other methods. This paper developed a multiobjective Big Data optimization approach based on a hybrid salp swarm algorithm and the differential evolution algorithm. The role of the differential evolution algorithm is to enhance the capability of the feature exploitation of the salp swarm algorithm because the operators of the differential evolution algorithm are used as local search operators. In general, the proposed method contains three stages. In the first stage, the population is generated, and the archive is initialized. The second stage updates the solutions using the hybrid salp swarm algorithm and the differential evolution algorithm, and the final stage determines the nondominated solutions and updates the archive. To assess the performance of the proposed approach, a series of experiments were performed. A set of single-objective and multiobjective problems from the 2015 Big Data optimization competition were tested; the dataset contained data with and without noise. The results of our experiments illustrated that the proposed approach outperformed other approaches, including the baseline nondominated sorting genetic algorithm, on all test problems. Moreover, for single-objective problems, the score value of the proposed method was better than that of the traditional multiobjective salp swarm algorithm. When compared with both algorithms, that is, the adaptive DE algorithm with external archive and the hybrid multiobjective firefly algorithm, its score was the largest. In contrast, for the multiobjective functions, the scores of the proposed algorithm were higher than that of the fireworks algorithm framework. [ABSTRACT FROM AUTHOR]

• New hybrid models are developed to predict scour depth downstream of a sluice gate. • Several input combinations are constructed for the prediction examinations. • The proposed models show robust scour depth prediction results. • The proposed models have better accuracy in comparison with the previously models. Sluice gate is a common tool to regulate water conveyance systems like irrigation channels or pipelines. The interaction between the flow and sediment particles downstream of the sluice gate may initiate scouring phenomenon and extend the resulted scour hole beneath the sluice gate foundation. The consequence of this procedure is undermining the whole structure, interrupting the flow passage, and regulation. Thus, the scour process downstream of a sluice gate is a critical point and robust scour depth prediction is still a crucial issue for hydraulic engineers. This paper proposes several novel hybrid adaptive neuro-fuzzy inference system (ANFIS) methods called ANFIS-PSO (particle swarm optimization), ANFIS-ACO (ant colony optimization), ANFIS-DE (differential evolution) and ANFIS-GA (genetic algorithm) as predictive models to estimate scour depth downstream of a sluice gate. To this end, some physical and hydraulic parameters such as d 50 (median diameter of bed material), b (gate opening), h (tail water depth), l (apron length), U (mean velocity of the jet) and σ g (geometric standard deviation of sediment grain size) are considered as predictive variables in form of non-dimensional parameters. To provide a reliable predictive model, three combinations of input variables are prepared by eliminating some predictive variables. To assess adequacy of proposed models, some error indices are employed in both training and testing phases. Results show the optimistic predictive model is ANFIS-PSO (RMSE = 0.437 and R 2 = 0.946) when all mentioned non-dimensional parameters are employed except h b. Furthermore, the proposed model has the largest accuracy compared to the previously developed AI and empirical models. Ultimately, it can be concluded that the hybrid ANFIS-PSO is a robust approach for scour depth prediction downstream of a sluice gate. [ABSTRACT FROM AUTHOR]

We propose a novel neural architecture search (NAS) approach for the challenge of designing convolutional neural networks (CNNs) that achieve a good tradeoff between complexity and accuracy. We rely on Cartesian genetic programming (CGP) and integrated real-based and block-chained CNN representation, for optimization using multi-objective evolutionary algorithms (MOEAs) in the continuous domain. We introduce two variants, CGP-NASV1 and CGP-NASV2, which differ in the granularity of their respective search spaces. To evaluate the proposed algorithms, we utilized the non-dominated sorting genetic algorithm II (NSGA-II) on the CIFAR-10, CIFAR-100,and SVHN datasets. Additionally, we extended the empirical analysis while maintaining the same solution representation to assess other searching techniques such as differential evolution (DE), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the S metric selection evolutionary multi-objective algorithm (SMS-EMOA). The experimental results demonstrate that our approach exhibits competitive classification performance and model complexity compared to state-of-the-art methods. • Evolutionary methods for NAS based on a CGP-based representation are proposed. • The CGP-encoding enables the use of off-the-shelf MOEAs to approach the problem. • Two versions, CGP-NASV1 and CGP-NASV2, differ in the granularity of the search space. • The results demonstrate our methods' effectiveness, while maintaining low complexity. [ABSTRACT FROM AUTHOR]

This paper investigates a hypothesis that in case of hard optimization problems, selection of the proper search space is more relevant than the choice of the solving algorithm. Dynamic Vehicle Routing Problem is used as a test problem and the hypothesis is verified experimentally on the well-known set of benchmark instances. The paper compares Particle Swarm Optimization (PSO) and Differential Evolution (DE) operating in two continuous search spaces (giving in total four distinctive approaches) and a state-of-the art discrete encoding utilizing Genetic Algorithm (GA). The advantage of selected continuous search space over a discrete one is verified on the basis of quality of the final solution and stability of intermediate partial solutions. During stability analysis it has been observed that practical level of uncertainty, resulting from the dynamic nature of the problem, is lower than could be expected from a popular degree of dynamism measure. In order to challenge that issue, benchmark problems have been solved also with a higher level of dynamism and an empirical degree of dynamism has been proposed as an additional measure of the amount of uncertainty of the problem. The results obtained by both continuous algorithms outperform those of state-of-the-art algorithms utilizing discrete problem representation, while the performance differences between them (PSO and DE) are minute. Apart from higher numerical efficiency in solving DVRP, the use of continuous problem encoding proved its advantage over discrete encoding also in terms of intermediate solutions stability. Requests-to-vehicles assignment sequences generated by the proposed approach were approximately 40% more accurate with respect to the final solution than their counterparts generated with a discrete encoding. Analysis of these sequences of intermediate solutions for several benchmark sets resulted in designing a penalty term taking into account a specific dynamic nature of the optimized problem. An addition of this penalty term improved the above-mentioned stability of partial solutions by another 10%. [ABSTRACT FROM AUTHOR]

In this work, novel application of evolutionary computational heuristics is presented for parameter identification problem of nonlinear Hammerstein controlled auto regressive auto regressive (NHCARAR) systems through global search competency of backtracking search algorithm (BSA), differential evolution (DE) and genetic algorithms (GAs). The mean squared error metric is used for the fitness function of NHCARAR system based on difference between actual and approximated design variables. Optimization of the cost function is conducted with BSA for NHCARAR model by varying degrees of freedom and noise variances. To verify and validate the worth of the presented scheme, comparative studies are carried out with its counterparts DE and GAs through statistical observations by means of weight deviation factor, root of mean squared error, and Thiel's inequality coefficient as well as complexity measures. • Evolutionary heuristics are explored for the first time to estimate NHCARAR parameters. • BSA establishes its worth over GA and DE for effective estimation in the NHCARAR model. • Robustness and efficacy of BSA is ascertained for noise varying noise scenarios of the model. • Validation of BSA is endorsed on the basis of different statistical performance metrics. • Complexity analyses indicate that BSA is efficient than GAs but comparable with DE. [ABSTRACT FROM AUTHOR]

In the present study, strength of meta-heuristic computing techniques is exploited for estimation problem of Hammerstein controlled auto regressive auto regressive moving average (HCARARMA) system using differential evolution (DE), genetic algorithms (GAs), pattern search (PS) and simulated annealing (SA) algorithms. The approximation theory in mean squared error sense is utilized for construction of cost function for HCARARMA model and highly uncorrelated adjustable parameter of the system is optimized with global search exploration of DE, GAs, PS and SA algorithms. Comparative study is carried out from desired known parameters of the HCARARMA model for different degree of freedom and noise variation scenarios. Performance analysis of the DE, GAs, PS and SA algorithms is conducted through results of statistics based on sufficient large independent executions in terms of measure of central tendency and variation for both precision and complexity indices. The exhaustive simulations established that the population-based heuristics are more accurate than single solution-based methodologies for HCARARMA identification. • Application of computational paradigm for parameter estimation problem of HCARARMA systems. • The population and single solution based algorithms are exploited for HCARARMA identification. • Validation on the basis of statistical performance indices for different noise scenarios and model dynamics. • Stability, reliability, extendibility, and wider applicability are other worthy perks of the scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-agent based art production framework. In existing artwork creation systems, images were generated using artificial life and evolutionary computation approaches. In artificial life, swarm intelligence or Boids model, and in evolutionary computation, genetic algorithm or genetic programming are commonly used to create images. These automated artwork creation systems make it easy to create artistic images even if the users are not professional artists. Despite the high possibility of these creation systems, however, much research has not been done so far. In this paper, we propose an art production framework that generates images using multi-agents with chaotic dynamics features. Agents act on the canvas following the three rules of Boids model. In addition, each agent possesses a chaotic neural network which trained by differential evolution algorithm, so that colors can be evolved to represent a better style. As a result, we propose an art production framework for generating processing artworks that contain highly complex dynamics. Finally, we created the glitch artworks using the proposed framework, which shows a new glitch style. [ABSTRACT FROM AUTHOR]

Highlights • Performance is comparable to Genetic Algorithms on assembly optimization. • Algorithms can handle complex nuclear assembly optimization problems. • Demonstrated no statistical performance impact due to control parameter sensitivity. Abstract Multi-objective optimization of nuclear engineering fuel assembly design problems is particularly difficult due to the highly non-linear interactions of a large number of possible variables. In addition, effective optimization algorithms are often highly problem-dependent and require extensive tuning, which reduces their applicability to the real world. To address this issue, Differential Evolution (DE) algorithms have been proposed as a new and effective method for heterogeneous fuel assembly optimization design problems. This paper presents the first complete study to investigate their applicability and performance. Firstly, two multi-objective DE algorithms have their performance compared against an Evolutionary Algorithm (EA) from the literature in optimizing a CORAIL mixed-oxide (MOX) fuel assembly for maximum plutonium content and minimum power peaking factor. Statistical analysis of the results shows the DE algorithms exhibit superior performance to the EA. The DE algorithms are then used to optimize a MOX fuel assembly with gadolinia poison, with results showing DE produces assembly designs comparable in performance to those in the literature. Finally, a sensitivity study is conducted on the control parameters of the better performing of the DE algorithms. Results indicate DE performance remains consistent for a wide range of values of both control parameters, suggesting the algorithm is able to perform effectively without requiring user expertise or effort to find the 'optimal' control parameter settings. [ABSTRACT FROM AUTHOR]

Abstract In this study, an efficient damage detection technique using the differential evolution (DE) algorithm and vibration data is proposed to properly detect the locations and extents of multiple damages of truss structures. The general equilibrium equations in which the reaction forces at notes are taken into account are considered. The compatibility equations in terms of forces are presented using the singular value decomposition (SVD) technique and then the extended equations of motion are developed based on the force method. As an optimization algorithm, the differential evolution (DE) algorithm is utilized and the objective function for damage detection is based on vibration data such as natural frequencies and mode shapes. Three numerical examples for planar and space truss structures are considered to verify the effectiveness and practical applicability of the present study. The numerical results show that the proposed method based on DE and vibration data can provide a reliable tool on determining the locations and extents of multiple damages of truss structures when it compares with those obtained from the genetic algorithm. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a novel saturated control method for a quadrotor to realize three–dimensional spatial trajectory tracking with heterogeneous comprehensive learning particle swarm optimization (HCLPSO). First, the model of the quadrotor is decomposed into cascaded control structure (CCS) with inner attitude control loop and outer position control loop. Second, the saturated control is deployed to confine the thrust force of the quadrotor in the outer position control loop. Then, the inner attitude reference signals can be generated with the outer position control loop through the cascaded control mode. Third, to alleviate the difficulty of the parameter adjustment, HCLPSO algorithm is applied in the control parameter optimization for the quadrotor. The optimization results of the HCLPSO are compared with that of particle swarm optimization (PSO), comprehensive learning particle swarm optimization (CLPSO), genetic algorithm (GA), and differential evolution (DE). At last, simulation results certify that the spatial trajectory tracking control of the quadrotor can be successfully achieved with the proposed method. [ABSTRACT FROM AUTHOR]

Abstract In smart cities, when the real-time control of traffic lights is not possible, the global optimization of traffic-light programs (TLPs) requires the simulation of a traffic scenario (traffic flows across the whole city) that is estimated after collecting data from sensors at the street level. However, the highly dynamic traffic of a city means that no single traffic scenario is a precise representation of the real system, and the fitness of any candidate solution (traffic-light program) will vary when deployed on the city. Thus, ideal TLPs should not only have an optimized fitness, but also a high reliability, i.e., low fitness variance, against the uncertainties of the real-world. Earlier traffic-light optimization methods, e.g., based on genetic algorithms, often simulate a single traffic scenario, which neglects variance in the real-world, leading to TLPs not optimized for reliability. Our main contributions in this work are the following: (a) the analysis of the importance of reliable solutions for TLP optimization, even when all traffic scenarios are consistent with the real-world data and highly correlated; (b) the adaptation of irace , an iterated racing algorithm that is able to dynamically adjust the number of traffic scenarios required to evaluate the fitness of TLPs and their reliability; (c) the use of a large real-world case study for which real-time control is not possible and where data was obtained from sensors at the street level; and (d) a thorough analysis of solutions generated by means of irace , a Genetic Algorithm, a Differential Evolution, a Particle Swarm Optimization and a Random Search. This analysis shows that simple strategies that simulate multiple traffic scenarios are able to obtain optimized solutions with improved reliability; however, the best results are obtained by irace , among the algorithms evaluated. Highlights • Adaptation of an elitist iterated racing with the goal of obtaining reliable traffic light programs. • Thorough analysis of solutions generated by means of 5 algorithms: IRACE, a GA, a DE, a PSO and a RS. • Use of a real-world case study which comes from sensors at the street level. • Future algorithms should consider the reliability of solutions over multiple traffic scenarios. • The hybridization of iterated racing strategies with evolutionary algorithms is one of the most promising directions. [ABSTRACT FROM AUTHOR]

Abstract Image segmentation is one of the fundamental techniques in image analysis. One group of segmentation techniques is based on clustering principles, where association of image pixels is based on a similarity criterion. Conventional clustering algorithms, such as k -means, can be used for this purpose but have several drawbacks including dependence on initialisation conditions and a higher likelihood of converging to local rather than global optima. In this paper, we propose a clustering-based image segmentation method that is based on the human mental search (HMS) algorithm. HMS is a recent metaheuristic algorithm based on the manner of searching in the space of online auctions. In HMS, each candidate solution is called a bid, and the algorithm comprises three major stages: mental search, which explores the vicinity of a solution using Levy flight to find better solutions; grouping which places a set of candidate solutions into a group using a clustering algorithm; and moving bids toward promising solution areas. In our image clustering application, bids encode the cluster centres and we evaluate three different objective functions. In an extensive set of experiments, we compare the efficacy of our proposed approach with several state-of-the-art metaheuristic algorithms including a genetic algorithm, differential evolution, particle swarm optimisation, artificial bee colony algorithm, and harmony search. We assess the techniques based on a variety of metrics including the objective functions, a cluster validity index, as well as unsupervised and supervised image segmentation criteria. Moreover, we perform some tests in higher dimensions, and conduct a statistical analysis to compare our proposed method to its competitors. The obtained results clearly show that the proposed algorithm represents a highly effective approach to image clustering that outperforms other state-of-the-art techniques. Highlights • A new method of image clustering is proposed using Human Mental Search (HMS). • Three objective functions are applied. • The efficacy of algorithm is examined with an extensive set of experiments. • The proposed algorithm represents a highly effective approach to image clustering. [ABSTRACT FROM AUTHOR]

Abstract We raise a few issues regarding time series modeling using Cognitive Maps, which is an example of a qualitative rather than a purely quantitative approach. Methods that operate at the level of concepts instead of numerical values are a worthy alternative for time series processing thanks to certain desirable properties such as abstraction and ease of interpretation. Those features are unavailable in classical time series modeling algorithms, where the main concern is numerical accuracy of a prediction expressed as a series of numerical values. In this work, we argue that it is desirable for Cognitive Map-based models to be simplified, ideally to the point where their descriptiveness and numerical accuracy is not compromised. In particular, we show experimentally that both the size of the map and the size of the set of possible weight values can be minimized without increasing the error rate. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other well-performing evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms' ability to optimize a multi-objective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio. Highlights • Evolutionary algorithms play significant role in demand response implementation. • Aggregator-consumer matching via evolutionary algorithms improves mutual benefits. • Two-archive strategy improves mutation and crossover speed in differential evolution. [ABSTRACT FROM AUTHOR]

Abstract The work presented here involves development and detailed investigations of niching methods for multimodal optimization of constrained functions. There is a lack of investigations in the literature on constrained multimodal optimization, hence a number of constrained niching algorithms have been developed here that leverage existing differential evolution-based niching methods with a feasibility rules-domination selection procedure. Furthermore, a suite of 18 benchmark functions has been developed and are presented in this paper; nine newly developed functions are incorporated with nine existing functions from the literature. Optimization results on these analytical functions using the constrained niching algorithms are presented, with analysis provided on the ability to locate multiple global optima, the convergence speed and constraint handling nature of the methods. The differential evolution strategy is also investigated, with SHADE and L-SHADE strategies considered. Finally, a dimensionality study also compares against the only other known constrained niching algorithm. Results indicate that all of the algorithms developed and tested generally perform well for low dimensional, low modality problems, but that local neighbourhood-based methods show the best results across the suite of functions tested. When high-dimensional problems are considered, using the L-SHADE strategy yields excellent results. An accompanying supplementary data file is provided with the manuscript. [ABSTRACT FROM AUTHOR]

Abstract Considered as cost-efficient, reliable and aesthetic alternatives to the conventional retaining structures, Mechanically Stabilized Earth Walls (MSEWs) have been increasingly used in civil engineering practice over the previous decades. The design of these structures is conventionally based on engineering guidelines, requiring the use of trial and error approaches to determine the design variables. Therefore, the quality and cost effectiveness of the design is limited with the effort, intuition, and experience of the engineer while the process transpires to be time-consuming, both of which can be solved by developing automated approaches. In order to address these issues, the present study introduces a novel framework to optimize the (i) reinforcement type, (ii) length, and (iii) layout of MSEWs for minimum cost, integrating metaheuristic optimization algorithms in compliance with the Federal Highway Administration guidelines. The framework is conjoined with optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE) and tested with a set of benchmark design problems that incorporate various types of MSEWs with different heights. The results are comparatively evaluated to assess the most effective optimization algorithm and validated using a well-known MSEW analysis and design software. The outcomes indicate that the proposed framework, implemented with a powerful optimization algorithm, can effectively produce the optimum design in a matter of seconds. In this sense, DE algorithm is proposed based on the improved results over GA, PSO, and ABC. Highlights • An automated framework is developed to design Mechanically Stabilized Earth Walls. • The design procedure is formulated as a mixed discrete-continuous variable problem. • The constraints are derived from stability and construction feasibility requirements. • Experiments yield cost efficient yet practically feasible design solutions. • Among many optimization algorithms, Differential Evolution is proposed for the problem. [ABSTRACT FROM AUTHOR]

Highlights • A new walking gait generator for biped robot is optimally identified by modified differential evolution (MDE) algorithm, namely adaptive evolutionary neural model (AENM). • Dynamic simulation of the biped locomotion combines inverse kinematics and ZMP principle. • Modelling result demonstrates the novel AENM approach ensures high performance for a robust and precise biped gait pattern generation. Abstract Gait generation plays a decisive role to the performance of biped robot walking. Under mathematical viewpoint, the task of gait generation design is investigated as an optimization problem with respect to various trade-off constraints, hence it prefers to evolutionary computation techniques. This paper introduces a novel approach for the biped robot gait generation which aims to enable humanoid robot to walk more naturally and stably on flat platform. The proposed modified differential evolution (MDE) optimisation algorithm is initiatively applied to optimally identify the novel adaptive evolutionary neural model (AENM) for a dynamic biped gait generator. The performance of proposed MDE method is demonstrated in comparison with other genetic algorithm (GA) and particle swarm optimisation (PSO) approaches. The proposed method is implemented and tested on a prototype small-sized humanoid robot. The identification result demonstrates that the new proposed neural AENM model proves an effective approach for a robust and precise biped gait generation. [ABSTRACT FROM AUTHOR]

Abstract The performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. A trend that has emerged recently is to make the algorithm parameters automatically adapt to different problems during optimization, thereby liberating the user from the tedious and time-consuming task of manual setting. These fine-tuning techniques continue to be the object of ongoing research. Differential evolution (DE) is a simple yet powerful population-based metaheuristic. It has demonstrated good convergence, and its principles are easy to understand. DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. In other words, this study describes in depth the structural analysis and working principle that underlie the promising and recent work in this field, to analyze their advantages and disadvantages and to gain future insights that can further improve these algorithms. Finally, the interpretation of the literature and the comparative analysis of the algorithmic schemes offer several guidelines for designing and implementing adaptive DE algorithms. The proposed design framework provides readers with the main steps required to integrate any proposed meta-algorithm into parameter and/or strategy adaptation schemes. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a decomposition-based multi-objective optimization approach considering multiple preferences, expressed by means of reference points, and with robust performance (mprMOEA/D). This algorithm is able to find multiple preferred regions in a single run, and its performance is robust with respect to different problems. The proposed algorithm utilizes a subpopulation (SP) for each reference point to search for the corresponding preferred region. An external population (EP) is maintained to selectively preserve solutions from all the SPs, and it can be revisited when producing new solution for each SP. The proposed collaboration mechanism between the SPs and EP is helpful in convergence and diversity preserving. In order to obtain robust performance, local crossover coordinate systems, which coincide with the local manifold of the Pareto set, are introduced into mprMOEA/D for the crossover operator of differential evolution, alleviating the influence of the overall Pareto set shape. The effects of these adopted techniques on the proposed algorithm are discussed, and the robust performance of the proposed approach is validated using numerical functions in comparison with four existing approaches. Experimental results show that the proposed algorithm outperforms the other algorithms. Highlights • A MOEA approach that can find multiple regions of interest is proposed. • A collaboration mechanism between subpopulations and an external one is used. • Local coordinate crossover operator for DE is introduced. [ABSTRACT FROM AUTHOR]

The simultaneous optimization of multiple objective functions is needed in many particle accelerator applications. In this paper, we present a parallel evolution based multi-objective optimizer that uses a variable population from generation to generation and an external storage to save good solutions. Two heuristic optimization methods, one uses the unified differential evolution and the other uses the real-coded genetic algorithm, are included in the optimizer to generate next generation candidate solutions, and are compared in the test examples. As an application, we applied this optimizer to the beam dynamics design optimization of a photoinjector and attained the optimal front solutions after 200 generations with the unified differential evolution offspring production scheme. [ABSTRACT FROM AUTHOR]

In joint replenishment problem with random number of imperfect items (IJRP), it is a great challenge for managers to correctly ascertain the number of imperfect items and take the right response strategy to make rectifications simultaneously. In this paper, the effectiveness of four common response strategies, i.e., scrap, all-unit quantity discount, rework & repair, and buy replacements for coping with imperfect items are investigated based on IJRP for the first time, respectively. Considering the NP -hard nature of the classical JRP and its extensions, as well as enhancing the explorable performance in crossing scale-levels of IJRP models and exploitable performance in diversifying the solution population of bare-bones differential evolutionary (BBDE), a meta-heuristic algorithm named as the adaptive BBDE (ABBDE) is designed by introducing an problem scale related adaptive recombination probability and a generalized opposition-based learning (OBL). Through numerical experiments, it has been found that (1) The adaptive improvements of ABBDE have been verified through obtaining the lower best-found total cost (TC), the lowest mean value, and the smallest standard error comparing to that of genetic evolution algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), bare-bones PSO (BBPSO), BBDE. (2) Parameter sensitivity analyses indicate that the scale of yearly demand, lead-time, and significance of uncertainty have significant impacts on system cost, whilst the parameters pertinent to specific response strategy are main factors causing system cost disturbance. (3) Management insights and limitations on adoption of a right response strategy and the practical usage of ABBDE are obtained. • Four response strategy influences for coping with defective items are examined. • Both random imperfect items and stochastic demands are considered. • An adaptive mutation probability and opposition-based learning are considered. • Numerical experiments are designed and extensively performed. • Insights are obtained through experiments on different scale cases. [ABSTRACT FROM AUTHOR]

This paper presents a nondominated sorting genetic algorithm III with three crossover strategies (NSGA-III-TCS) for the combined heat and power dynamic economic emission dispatch (CHPDEED) problems with or without prohibited operating zones. The first strategy updates the individuals of the first nondomination rank by a modified improvisation of the harmony search algorithm, which accelerates the information exchanges of the elite individuals. The second strategy regulates the individuals of the last nondomination rank according to a modified single-point crossover of the genetic algorithm, which strengthens the convergence of the population. The third strategy adjusts the remaining individuals in terms of a modified DE/rand-to-best/1 mutation of the differential evolution algorithm, and it guides these individuals to move towards the ones which have at least one minimum objective function value, which is beneficial for expanding the distribution range of the population. NSGA-III-TCS obtains the largest average hypervolumes and shows a desirable convergence for six CHPDEED problems. NSGA-III-TCS achieves relatively high coverage rates in most cases, indicating the dominance of the Pareto sets of NSGA-III-TCS over those of the other algorithms. Also, it has relatively low average spacings for all problems, suggesting the good evenness of the Pareto sets. • Combined heat and power dynamic economic emission dispatch (CHPDEED) is studied. • A NSGA-III with three crossover strategies (NSGA-III-TCS) is presented for CHPDEED. • The Pareto sets of the NSGA-III-TCS have high hypervolumes and coverage rates. • NSGA-III-TCS obtains good solutions with low generation cost and pollutant emission. [ABSTRACT FROM AUTHOR]

Many multi-objective evolutionary algorithms are proposed to handle constrained multi-objective optimization problems. Nevertheless, they often fail to appropriately balance feasibility, convergence and diversity of the population. This paper proposed a dynamic dual-population co-evolution multi-objective evolutionary algorithm (DDCMEA) to solve this issue. In DDCMEA, a dynamic dual-population co-evolution strategy is employed to balance the convergence and the feasibility by dynamically adjusting the offspring number of the two populations. In the early stage of evolution, the algorithm mainly focuses on the convergence and more offspring of the first population are generated. In the late stage of evolution, the algorithm mainly focuses on the feasibility and more offspring of the second population are generated. Finally, feasible solutions with good convergence could be obtained. To further enhance the diversity of the offspring and obtain feasible solutions with a wide spread of distribution, the evolution operators of the genetic algorithm and the differential evolution are chosen as the search engines for the first population and the second population, respectively. The performance of DDCMEA is further tested through thirty-one bench-mark test problems and two real-world problems in comparison with other five state-of-the-art algorithms. The results show the proposed algorithm DDCMEA achieves competitive performance when handling constrained multi-objective optimization problems. • A dual-population MOEA for CMOPs. • Dynamic dual-population co-evolution. • Two evolutionary search engines are applied. • Offspring number dynamically adjusted. [ABSTRACT FROM AUTHOR]

Customer satisfaction is crucially affected by energy consumption in mobile devices. One of the most energy-consuming parts of an application is images. This paper, first, investigates that there is a correlation between energy consumption and image quality as well as image file size. Therefore, these two can be considered as a proxy for energy consumption. In the next step, we focused on proposing a multi-objective strategy to enhance image quality and reduce image file size based on the quantisation table (QT) in JPEG image compression. To this end, we have used two general multi-objective approaches: scalarisation and Pareto-based. In this paper, we embed our strategy into five scalarisation algorithms, including energy-aware multi-objective genetic algorithm (EnMOGA), energy-aware multi-objective particle swarm optimisation (EnMOPSO), energy-aware multi-objective differential evolution (EnMODE), energy-aware multi-objective evolutionary strategy (EnMOES), and energy-aware multi-objective pattern search (EnMOPS). Also, two Pareto-based methods, including a non-dominated sorting genetic algorithm (NSGA-II) and a reference-point-based NSGA-II (NSGA-III) are used for the embedding scheme, and two Pareto-based algorithms, EnNSGAII and EnNSGAIII, are presented. With our proposed scalarisation method, user's preferences can be set before starting the optimisation process and the algorithm generates only one solution based on the preference, while our Pareto-based approaches generate a set of solutions so that a user can select one of the preferred solutions after the optimisation process. Experimental studies show that the performance of the baseline algorithm is improved by embedding the proposed strategy into metaheuristic algorithms. In particular, EnMOGA, EnMOPS, and EnNSGA-II can perform competitively, among others. From the results, the baseline algorithm in all cases and in comparison to all algorithms yields the worst results. Among the scalarisation methods, EnMOGA and EnMOPS can achieve the first rank in 6 and 7 out of 13 cases and the second rank in 7 and 5 cases in terms of objective function. Also, EnMOES achieved the fifth or worst rank among the scalarisation algorithms. Regarding the Pareto-based algorithms, the table shows that EnNSGAII outperforms EnNSGAIII in 10 out of 13 cases in terms of hyper-volume measure, while it fails in 3 cases. Furthermore, we statistically verify the proposed algorithm's effectiveness based on the Wilcoxon-signed rank test. Finally, a sensitivity analysis of the parameters is provided. The source code for reproducing the results is available in: https://github.com/SeyedJalaleddinMousavirad/MultiobjectiveJPEGImageCompression. • Customer satisfaction is crucially affected by energy consumption in mobile devices. • There is a correlation between energy consumption, image quality and image file size. • We introduce a multi-objective formulation for JPEG image compression. • The proposed strategy is embedded into scalarisation and Pareto-based approaches. [ABSTRACT FROM AUTHOR]

Abstract In today's high competitive industry, it is imperative to achieve high level production rates of high quality products. Manufacturers with efficient process predictabilities are better equipped to gain a competitive edge in the market. Surface finish is one of the most important measures for determining the quality of products in machining. This work presents a comparative study utilizing several Artificial intelligence (AI) techniques to predict surface roughness in turning. Extensive experimental work was conducted to obtain vibration signals during the turning process of steel rods at different speeds, feeds and depth of cut (DOC) conditions. The vibration signals are then analyzed using several spectral and statistical techniques to extract features which are then used as inputs to the AI algorithms. Surface roughness measures are divided into three classes; smooth, medium and rough surface finish. Three AI methods are compared for their effectiveness in predicting the surface roughness class from the vibration signals. The methods used are canonical genetic algorithm, particle swarm optimization (PSO) and differential evolution (DE) algorithms. Six different feature sets from vibration data were used and preliminary results show the effectiveness of the combination of particular feature sets and the differential evolution algorithm in predicting classes. [ABSTRACT FROM AUTHOR]

Abandoned areas with substances potentially harmful to the environment or human health have raised numerous concerns around the world. The objective of the present study is to analyze the possibility of building solar power plants capable of capturing solar energy in unproductive areas, both contaminated and uncontaminated. For this purpose, American data from the National Solar Radiation Database (NSRDB) were used, a collection of hourly solar radiation measurements and meteorological data, as well as data from the RE-Powering America's Land project, run by the United States Environmental Protection Agency (EPA). In the analysis, the information about “mapped area”, “distances to the transmission lines”, “solar direct normal irradiance on a utility scale” and “off-grid direct normal irradiance” were considered. To define the best locations, the data were initially pre-processed. A new hybrid fuzzy c-means (HFCM) algorithm was then applied, initialized, comparatively, by three metaheuristics: Differential Evolution (DE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), for the data clustering. The number of clusters obtained was validated by three metrics: Calinski-Harabasz Criterion, Davies-Bouldin Criterion and Silhouette Coefficient, with all three unanimously indicating two clusters as the ideal number: one cluster for locations with greater potential for allocating facilities to capture solar energy, and another for locations with a lower potential. With the new approach, an increase of 23.3% in the training velocity of the HFCM algorithm was identified, which required fewer iterations to achieve the same value of the objective function. Else, a round of experiments was conducted with six different datasets (instances from literature) and the results showed that the proposed method can achieve better results (faster convergence and smaller solution cost) than the classic FCM. Visually, the predominance of the allocation of facilities was perceived in states with a greater average incidence of solar radiation. Therefore, this was the predominant factor in the convergence of the algorithm, which is in accordance with expectations for solar energy. Finally, the social, economic and environmental gains were considered with the revitalization of unproductive land with the possibility of implementing solar power plants in these areas. [ABSTRACT FROM AUTHOR]