A novel Whale Optimization Algorithm integrated with Nelder–Mead simplex for multi-objective optimization problems

Recently, several meta-heuristics and evolutionary algorithms have been proposed for tackling optimization problems. Such methods tend to suffer from degraded performance when solving multi-objective optimization problems due to addressing the conflicting goals of finding accurate estimation of Pare...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 212; p. 106619
Main Authors: Abdel-Basset, Mohamed, Mohamed, Reda, Mirjalili, Seyedali
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 05.01.2021
Elsevier Science Ltd
Subjects:
Algorithm
Algorithms
Artificial Intelligence
Convergence
Dynamic distance
Evolutionary algorithms
Experiments
Heuristic
Multi-objective optimization
Multiple objective analysis
Nelder–Mead method
Novels
Objectives
Opposition based learning
Optimization
Optimization algorithms
Pareto optimization
Pareto optimum
Performance degradation
Swarm Intelligence
Tests
Whale Optimization Algorithm
Whales & whaling
WOA
Nelder–Mead method
Whale Optimization Algorithm
Artificial Intelligence
WOA
Multi-objective optimization
Dynamic distance
Algorithm
Opposition based learning
Optimization
Swarm Intelligence
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:Recently, several meta-heuristics and evolutionary algorithms have been proposed for tackling optimization problems. Such methods tend to suffer from degraded performance when solving multi-objective optimization problems due to addressing the conflicting goals of finding accurate estimation of Pareto optimal solutions and increasing their distribution across all objectives. In this paper, the Whale Optimization Algorithm (WOA) is improved and extended to solve such multi-objective optimization problems with the purpose of alleviating these drawbacks. The improvements include: (1) modifying the distance control factor of the standard WOA to contain values generated dynamically instead of a fixed one, (2) the trade-off between moving toward the opposite of the best solution and its original values based on a certain probability to prevent stuck into local minima, and (3) accelerating the convergence and coverage using Nelder–Mead method and the Pareto Archived Evolution Strategy (PAES). The proposed algorithm is tested on three benchmark multi-objective test functions (DTLZ, CEC 2009, and GLT), including 25 test functions, to verify its effectiveness by comparing with nine robust multi-objective algorithms. The experiments demonstrate the superiority of the proposed algorithm compared to some of the existing multi-objective algorithms in the literature.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2020.106619