Sequential Multi-objective Genetic Algorithm

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Falahiazar Leila; Seydi Vahid; Mirzarezaee Mitra

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Journal Paper

Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2021 | Volume:9 | Issue:3 Page(s): 369-381

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Information Journal Paper

Title

Sequential Multi-objective Genetic Algorithm

Author(s)

Falahiazar Leila | Seydi Vahid | Mirzarezaee Mitra | Issue Writer Certificate

Keywords

Multi-objective Evolutionary Algorithms

Non-dominated Sorting Genetic Algorithm II

Sequential Multi-objective Algorithm

Benchmark Functions

T-test

Abstract

Many real-world issues have multiple conflicting objectives, and optimization of the contradictory objectives is very difficult. In the recent years, the Multi-objective Evolutionary Algorithms (MOEAs) have shown a great performance in order to optimize such problems. Thus the development of MOEAs will always lead to the advancement of science. The Non-dominated Sorting Genetic Algorithm II (NSGAII) is considered as one of the most used evolutionary algorithms, and many MOEAs such as the Sequential Multi-objective Algorithm (SEQ-MOGA) have emerged to resolve the NSGAII problems. SEQ-MOGA presents a new survival selection that arranges the individuals systematically, and the chromosomes can cover the entire Pareto Front region. In this work, the Archive Sequential Multi-objective Algorithm (ASMOGA) is proposed in order to develop and improve SEQ-MOGA. ASMOGA uses the archive technique in order to save the history of the search procedure so that the maintenance of the diversity in the decision space is adequately satisfied. In order to demonstrate the performance of ASMOGA, it is used and compared with several state-of-the-art MOEAs for optimizing the Benchmark Functions and designing the I-Beam problem. The optimization results are evaluated by the performance metrics such as the hyper-volume, generational distance, spacing, and T-test (a statistical test). Based on the results obtained, the superiority of the proposed algorithm is clearly identified.

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APA: Copy

Falahiazar, Leila, Seydi, Vahid, & Mirzarezaee, Mitra. (2021). Sequential Multi-objective Genetic Algorithm. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 9(3 ), 369-381. SID. https://sid.ir/paper/992811/en

Vancouver: Copy

Falahiazar Leila, Seydi Vahid, Mirzarezaee Mitra. Sequential Multi-objective Genetic Algorithm. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2021;9(3 ):369-381. Available from: https://sid.ir/paper/992811/en

IEEE: Copy

Leila Falahiazar, Vahid Seydi, and Mitra Mirzarezaee, “Sequential Multi-objective Genetic Algorithm,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 9, no. 3 , pp. 369–381, 2021, [Online]. Available: https://sid.ir/paper/992811/en

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