An Improved Artificial Electric Field Algorithm for Multi-Objective Optimization

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.

Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

“…As a result, a variety of optimization problems have been successfully solved using this algorithm. Controller design 36 , multi-objective optimization problems 37 , soil shear strength prediction 38 , pattern search 39 , vehicle routing 40 and tumor detection 17 are some of the examples of the research problems solved by AEFA. To improve performance and address the shortcomings of the AEFA, numerous academics have developed variants of the original AEFA in recent years.…”

Artificial Electric Field Algorithm with Greedy State Transition Strategy for Spherical Multiple Traveling Salesmen Problem

“…40 The optimal planning of 5 salesmen in 100 cities Fig. 41 The optimal planning of 5 salesmen in 150 cities Fig. 42 The optimal planning of 5 salesmen in 200 cities Fig.…”

“…Its main characteristics include less optimization parameters, rapid convergence in the process of optimization, high accuracy, and low complexity of the algorithm [37]. Since its inception, AEFA has been widely used in continuous optimization problems [38], practical engineering problems [39], image matching problems [40], multi-objective optimization [41], nine parameters triode PV model best estimate [42], cancer detection [43], white blood cells [44], the optimal purchasing problem [45], feature classification [46], assembly line balancing problem [47], and wind turbines active loss in distribution network and the distribution of the voltage deviation problems [48] made a wide range of applications. In this paper, an improved AEFA is proposed.…”

“…39,40,41,42,43,44, and 45 intuitively show the optimal path searched by GSTAEFA. All cities and routes can be seen simultaneously in transparent mode.…”

Improved artificial electric field algorithm based on multi-strategy and its application

“…The spatial dimension is 30. The evaluation results are from the optimal value, the worst value, the average value, the standard deviation, and the running time, and the optimum values are indicated in bold type [19].…”