An Efficient Parallel Version of Dynamic Multi-Objective Evolutionary Algorithm

Author:

Grid Maroua,Belaiche Leila,Kahloul Laid,Benharzallah Saber

Abstract

Multi-objective optimization evolutionary algorithms (MOEAs) belong to heuristic methods proposed for solving multi-objective optimization problems (MOPs). In fact, MOEAs search for a uniformly distributed, near-optimal, and near-complete Pareto front for a given MOP. However, several MOEAs fail to achieve their aim completely due to their fixed population size. To overcome this shortcoming, Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) [20] was proposed. Although DMOEA has the distinction of dynamic population size, it still suffers from a long execution time. To deal with the last disadvantage, we have proposed previously a Parallel Dynamic Multi-Objective Evolutionary Algorithm (PDMOEA) [10] to obtain efficient results in less execution time than the sequential counterparts, in order to tackle more complex problems. This paper is an extended version of [10] and it aims to demonstrate the efficiency of PDMOEA through more experimentations and comparisons. We firstly compare DMOEA with other multi-objective evolutionary algorithms Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm ( SPEA-II), then we present an exhaustive comparison of PDMOEA versus DMOEA and discuss how the number of used processors influences the efficiency of PDMOEA. As experimental results, PDMOEA enhances DMOEA in terms of three criteria: improving the objective space, minimizing the computational time, and converging to the desired population size. Finally, the paper establishes a new formula relating the suitable number of processes, required in PDMOEA, and the number of necessary generations to converge to the optimal solutions.

Publisher

Zarqa University

Subject

General Computer Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Parallel Multi-Objective Evolutionary Algorithm for Constrained Multi-Objective Optimization;2023 24th International Arab Conference on Information Technology (ACIT);2023-12-06

2. Optimization System Design of Building Internal Structure Based on Multi-Objective Evolutionary Algorithm;2022 International Conference on Knowledge Engineering and Communication Systems (ICKES);2022-12-28

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