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This paper develops a robust strategy based on integrating three mutation phases and adapted control parameters into the Adaptive Guided Differential Evolution algorithm called (mAGDE) to improve diversity and exploration of the original AGDE. The mAGDE performance is evaluated using IEEE CEC'2020 test suite. Furthermore, the mAGDE is employed to identify the solid oxide fuel cell (SOFC) model optimal parameters. Two modes of SOFC operation are investigated, steady and transient states. The results obtained from the proposed mAGDE are compared with a number of recent, well-established and reputed meta-heuristics, including Particle swarm optimization, Teaching learning-based optimization, Whale optimization algorithm, Harris hawks optimization, Marine predators algorithm, Archimedes optimization algorithm, Differential evolution, and the original AGDE. Additionally, the statistical parameters that measure the performance of the proposed optimizer and the other competitors are calculated. The main finding demonstrated the preference and robustness of the suggested mAGDE in constructing the SOFC circuit that closely converges to the actual one. During the steady-state operation, the best fitness value obtained via the suggested mAGDE for operation at 1273 K is 2.2995E−06, while in the transient-state operation, the best SMSE is 1.04. The average cost function is decreased by 43.33% compared to the one obtained by the original AGDE. From the aforementioned assessments, it can be concluded that the proposed mAGDE is outstanding and promising. • This paper introduces a modified mAGDE based on the integration of a modified three-phases AGDE with the DE_SPA algorithm. • A modified three-phases AGDE has been introduced to maintain diversity and convergence of the evolutionary process. • The CEC'2020 test suite problems are used to demonstrate the performance mAGDE against other competitors. • Applying for first time mAGDE to identify the optimal parameters of SOFC model. • The robustness of the proposed strategy-based mAGDE is proved. [ABSTRACT FROM AUTHOR]

Bionic intelligence algorithms have many advantages compared with traditional optimization algorithms. In this paper, inspired by the growth law of trees, a new bionic algorithm, named artificial tree (AT) algorithm is developed. In the proposed AT, the branch position is considered as the design variable. In addition, the branch is the solution, and the branch thickness is the indicator of the solution. The computing process of AT is achieved by simulating the transport of organic matters and the update of tree branches. The comparative analysis using thirty typical benchmark problems between AT algorithm and some well-known bionic intelligent methods is also performed. Based on numerical results, AT is found to be very effective in dealing with various problems. [ABSTRACT FROM AUTHOR]

Abstract: The concept of particle swarms, although initially introduced for simulating human social behaviors, has become very popular these days as an efficient means for intelligent search and optimization. The particle swarm optimization (PSO), as it is called now, does not require any gradient information of the function to be optimized, uses only primitive mathematical operators and is conceptually very simple. This paper investigates a novel approach to the designing of two-dimensional zero phase infinite impulse response (IIR) digital filters using the PSO algorithm. The design task is reformulated as a constrained minimization problem and is solved by a modified PSO algorithm. Numerical results are presented. The paper also demonstrates the superiority of the proposed design method by comparing it with two recently published filter design methods and two other state of the art optimization techniques. [Copyright &y& Elsevier]

Coverage plays a vital role in the performance and proper functioning of wireless sensor networks. However, ensuring a network's coverage is met numerous challenges due to sensors having limited sensing range, communication range, and energy. Many coverage problems are NP-hard, one of which is the network coverage with lifetime problem (CTLP). As such, a number of meta-heuristic algorithms have been proposed to solve CTLP in practical scenarios. This paper proposes an approach for CTLP based on the teaching–learning based optimization algorithm (TLBO), which is often employed to address continuous optimization problems. Specifically, a discrete version of multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO) called HTLBO is proposed, employing genetic operators inspired by evolutionary computing methods. Experimental results are extensively compared to those obtained from previous approaches, namely MO-ITLBO, fast elitist non-dominated sorting genetic algorithm (NSGA-II), multi-objective differential evolution (MODE), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The evaluation shows significant improvements in different metrics, including spacing, hypervolume, non-dominated solutions, and coverage. • We investigate the problem of optimal sensor node placement with three objectives: (i) minimize the number of deployed sensor nodes, (ii) maximize the k -coverage metric, and (iii) maximize the network lifetime. • We propose a hybrid algorithm combining teaching–learning based optimization and evolutionary computing for multi-objective sensor placement. The proposed algorithm introduces multiple teachers to improve the learners' results in different subjects. Moreover, a learner interacts with other learners through a crossover operator. • We compare the proposed algorithm with existing methods, including MODE, MOEAD, MO-TLBO, and NSGA-II on C-metric, spacing-metric, hypervolume-metric, and non-dominated solutions metric to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Abstract: Parameter optimization of proton exchange membrane fuel cell (PEMFC) model has received considerable attention recently. In order to estimate the unknown parameters of PEMFC model faster and obtain more accurate solutions, in this paper, an improved multi-strategy adaptive differential evolution (DE) is presented for the parameter optimization problems of PEMFC model. The approach is referred to as rank-MADE, for short. In rank-MADE, the multiple mutation strategies of DE are adaptively selected to avoid choosing a suitable strategy for a specific problem by trial-and-error method. Furthermore, the ranking-based vector selection technique is employed in different mutation strategies to accelerate the process of parameter optimization of PEMFC model. In order to verify the performance of rank-MADE, it is applied to estimate the parameters of the Ballard Mark V FC, the SR-12 Modular PEM Generator, the BCS 500-W stack, the Temasek FC, and the WNS-FC model. In addition, rank-MADE is compared with other advanced DE variants and other evolutionary algorithms (EAs). Experimental results show that rank-MADE is able to provide higher quality of solutions, faster convergence speed, and higher success rate compared with other DE variants. Additionally, the V–I characteristics obtained by rank-MADE agree well with the experimental data in all cases. Therefore, rank-MADE can be an effective alternative in the field of other complex parameter optimization problems of fuel cell models. [Copyright &y& Elsevier]

Abstract: This paper presents a constrained Self-adaptive Differential Evolution (SaDE) algorithm for the design of robust optimal fixed structure controllers with uncertainties and disturbance. Almost all real world optimization problems have constraints which should be satisfied along with the best optimal solution for the problem. In evolutionary algorithms (EAs) the presence of constraints reduces the feasible region and complicates the search process. Therefore, a suitable method to handle the constraints must also be executed. In the SaDE algorithm, four mutation strategies and the control parameter CR are self-adapted. Self-adaptive Penalty (SP) method is introduced into the SaDE algorithm for constraint handling. The performance of SaDE algorithm is demonstrated on the design of robust optimal fixed structure controller of three systems, namely the linearized magnetic levitation system, F-8 aircraft linearized model and a SISO plant. For the comparison purpose, reported results of constrained PSO algorithm and five DE algorithms with different strategies and parameter values are taken into account. Statistical performance in 20 independent runs is considered to compare the performance of algorithms. From the obtained results, it is observed that SaDE algorithm is able to self-adapt the mutation strategy and the crossover rate and hence performs better than the other variants of DE and the constrained PSO algorithm. Better performance of SaDE is achieved by sustained maintenance of diversity throughout the evolutionary process thus producing better individuals consistently. This also aids the algorithm to escape from local optima thereby avoiding premature convergence. [Copyright &y& Elsevier]

Abstract: In this paper, an effective and reliable algorithm, termed as evolving ant direction differential evolution (EADDE) algorithm, for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics is presented. In this method, suitable mutation operator for differential evolution (DE) is found by ant colony search. The genetic algorithm evolves the ant colony parameters and the Newton–Raphson method solves the power flow problem. The proposed algorithm has been examined on the standard IEEE 30-bus and IEEE 57-bus systems with three different objective functions. Different cases were considered to investigate the robustness of the proposed method in finding the global solution. The EADDE provides better results compared to classical DE and other methods recently reported in the literature as demonstrated by simulation results. [Copyright &y& Elsevier]

Abstract: The multilevel thresholding problem is often treated as a problem of optimization of an objective function. This paper presents both adaptation and comparison of six meta-heuristic techniques to solve the multilevel thresholding problem: a genetic algorithm, particle swarm optimization, differential evolution, ant colony, simulated annealing and tabu search. Experiments results show that the genetic algorithm, the particle swarm optimization and the differential evolution are much better in terms of precision, robustness and time convergence than the ant colony, simulated annealing and tabu search. Among the first three algorithms, the differential evolution is the most efficient with respect to the quality of the solution and the particle swarm optimization converges the most quickly. [Copyright &y& Elsevier]

In practical applications, solving multimodal multiobjective optimization problems (MMOPs), which have multiple Pareto optimal sets (PSs) in the decision space mapping to the same Pareto front (PF) in the objective space, has great significance for decision makers. The issue of how to maintain diversity in both decision space and objective space remains a key problem for existing multimodal multiobjective evolutionary algorithms. To address this issue, a novel grey prediction evolution algorithm for multimodal multiobjective optimization, termed MMGPE, is proposed in this paper. This is the first time that the grey prediction evolution algorithm (GPE), which is a recently proposed competitive optimization algorithm with strong exploration capability, is improved for MMOPs. These improvements are conducted in the following four aspects: (1) an initialization operator based on particle swarm optimization, (2) an adaptive parameter setting strategy depending on the domain of decision variables of MMOPs, (3) an accelerating convergence mechanism inspired by the niche principle, and (4) an environmental selection operator based on a nondominated sorting mechanism and a special crowding distance approach. The performance of MMGPE is compared with two state-of-the-art multiobjective evolutionary algorithms and four multimodal multiobjective evolutionary algorithms on 11 multimodal multiobjective test functions. MMGPE is also applied to solve a practical problem. The results show the MMGPE's effectiveness and superiority in achieving the goal of finding multiple PSs while obtaining a well-distributed PF. [ABSTRACT FROM AUTHOR]