Abstract
Real-world problems such as scientific, engineering, mechanical, etc., are multi-objective optimization problems. In order to achieve an optimum solution to such problems, multi-objective optimization algorithms are used. A solution to a multi-objective problem is to explore a set of candidate solutions, each of which satisfies the required objective without any other solution dominating it. In this paper, a population-based metaheuristic algorithm called an artificial electric field algorithm (AEFA) is proposed to deal with multi-objective optimization problems. The proposed algorithm utilizes the concepts of strength Pareto for fitness assignment and the fine-grained elitism selection mechanism to maintain population diversity. Furthermore, the proposed algorithm utilizes the shift-based density estimation approach integrated with strength Pareto for density estimation, and it implements bounded exponential crossover (BEX) and polynomial mutation operator (PMO) to avoid solutions trapping in local optima and enhance convergence. The proposed algorithm is validated using several standard benchmark functions. The proposed algorithm’s performance is compared with existing multi-objective algorithms. The experimental results obtained in this study reveal that the proposed algorithm is highly competitive and maintains the desired balance between exploration and exploitation to speed up convergence towards the Pareto optimal front.
Highlights
Most realistic problems in science and engineering consist of diverse competing objectives that require coexisting optimization to obtain a solution
The obtained values competed with the values of gravitational search algorithm (GSA) and outperformed the value of artificial bee colony (ABC), cuckoo search algorithm (CK), and backtracking search algorithm (BSA), which reveals that artificial electric field algorithm (AEFA) maintains a balance between exploration and exploitation and performs better in comparison to existing approaches
(except benchmark F10) functions, AEFA performs worse as compared to ABC, and BSA, which implies that the AEFA suffers from the loss of diversity resulting in premature convergence in solving complex
Summary
Most realistic problems in science and engineering consist of diverse competing objectives that require coexisting optimization to obtain a solution. Realistic multiple objective problems (MOOPs) require a long time to evaluate each objective function and constraint. In solving such realistic MOOPs, the stochastic process shows more competence and suitability than conventional methods. GA has been used to solve MOOPs, called non-dominated sorting genetic algorithm (NSGA-II) [3]. Processes 2020, 8, 584 multi-objective optimization problems These multi-objective approaches in one computation can produce several evenly distributed candidate solutions rather than a single solution. This paper is organized in the following way: Section 2 covers an overview of the existing literature on multi-objective optimization, Section 3 describes the preliminaries and background algorithms, Section 4 describes the proposed multi-objective method in detail, Section 5 presents results and performance of the proposed algorithm in comparison to existing optimization techniques, and Section 6 sums up the findings of this research in concluding remarks
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