MOEA/UE: A novel multi-objective evolutionary algorithm using a uniformly evolving scheme

Wang, Zhicang; Li, Hecheng; Yu, Hao

doi:10.1016/j.neucom.2020.04.149

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…Also, Mohamed Abdel-Basset et al [21] enhanced the equilibrium algorithm for resolving multiobjective problems based on the archive approach; to maintain diversity among the nondominated solutions, Pareto optimal solutions and the crowding distance metric were also used. Wang et al [22] introduced a multiobjective evolutionary method incorporated by a uniformly evolving system to locate nondominated solutions that are homogeneously distributed on the true Pareto optimal curve to provide flexibility of the decision makers while choosing the suitable solutions. Swarm intelligence-based metaheuristic algorithms are based generally on the homogeneous movement of an agent.…”

Section: Introductionmentioning

confidence: 99%

A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

Chalabi,

Attia,

Alnowibet

et al. 2023

Mathematics

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Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of the recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective gaining–sharing knowledge optimization (MOGSK), to deal with multiobjective optimization problems (MOPs). MOGSK employs an external archive population to store the nondominated solutions generated thus far, with the aim of guiding the solutions during the exploration process. Furthermore, fast nondominated sorting with crowding distance was incorporated to sustain the diversity of the solutions and ensure the convergence towards the Pareto optimal set, while the ϵ-dominance relation was used to update the archive population solutions. ϵ-dominance helps provide a good boost to diversity, coverage, and convergence overall. The validation of the proposed MOGSK was conducted using five biobjective (ZDT) and seven three-objective test functions (DTLZ) problems, along with the recently introduced CEC 2021, with fifty-five test problems in total, including power electronics, process design and synthesis, mechanical design, chemical engineering, and power system optimization. The proposed MOGSK was compared with seven existing optimization algorithms, including MOEAD, eMOEA, MOPSO, NSGAII, SPEA2, KnEA, and GrEA. The experimental findings show the good behavior of our proposed MOGSK against the comparative algorithms in particular real-world optimization problems.

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Section: Introductionmentioning

confidence: 99%

A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

Chalabi,

Attia,

Alnowibet

et al. 2023

Mathematics

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show abstract

“…Wang [47] proposed a multiobjective evolutionary algorithm integrated with a uniformly evolving scheme to produce non-dominated solutions uniformly distributed on the true Pareto optimal curve to give the decision-makers the flexibility in selecting the satisfactory solutions. Many other multi-objective algorithms are designed for dealing with MOPs such as flower pollination algorithm [48], symbiotic organism search [49], bat algorithm [50], ant lion optimizer [51], whale algorithm [52] Recently, a new meta-heuristic optimization algorithm called the marine predators algorithm (MPA) has been proposed for solving continuous optimization problems [68].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Marine Predators Algorithm for Solving Multi-Objective Optimization Problems: Analysis and Validations

et al. 2021

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Recently, a new strong optimization algorithm called marine predators algorithm (MPA) has been proposed for tackling the single-objective optimization problems and could dramatically fulfill good outcomes in comparison to the other compared algorithms. Those dramatic outcomes, in addition to our recently-proposed strategies for helping meta-heuristic algorithms in fulfilling better outcomes for the multi-objective optimization problems motivate us to make a comprehensive study to see the performance of MPA alone and with those strategies for those optimization problems. Specifically, This paper proposes four variants of the marine predators' algorithm (MPA) for solving multi-objective optimization problems. The first version, called the multi-objective marine predators' algorithm (MMPA) is based on the behavior of marine predators in finding their prey. In the second version, a novel strategy called dominance strategybased exploration-exploitation (DSEE) recently-proposed is effectively incorporated with MMPA to relate the exploration and exploitation phase of MPA to the dominance of the solutions-this version is called M-MMPA. DSEE counts the number of dominated solutions for each solution-the solutions with high dominance undergo an exploitation phase; the others with small dominance undergo the exploration phase. The third version integrates M-MMPA with a novel strategy called Gaussian-based mutation, which uses the Gaussian distribution-based exploration and exploitation strategy to search for the optimal solution. The fourth version uses the Nelder-Mead simplex method with M-MMPA (M-MMPA-NMM) at the start of the optimization process to construct a front of the non-dominated solutions that will help M-MMPA to find more good solutions. The effectiveness of the four versions is validated on a large set of theoretical and practical problems. For all the cases, the proposed algorithm and its variants are shown to be superior to a number of well-known multi-objective optimization algorithms.

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Optimal DSTATCOM, PVAs and WTGUs allocation for electrical distribution system performance improvement using improved TLBO

Ramana¹,

Reddy

Yadlapati

et al. 2023

Int J Syst Assur Eng Manag

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MOEA/UE: A novel multi-objective evolutionary algorithm using a uniformly evolving scheme

Cited by 6 publications

References 35 publications

A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

An Efficient Marine Predators Algorithm for Solving Multi-Objective Optimization Problems: Analysis and Validations

Optimal DSTATCOM, PVAs and WTGUs allocation for electrical distribution system performance improvement using improved TLBO

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