MO-NFSA for solving unconstrained multi-objective optimization problems

Normative Fish Swarm Algorithm (NFSA) is a novel variant of the Artificial Fish Swarm Algorithm (AFSA) proposed in 2019, and has been proven effective in solving single-objective optimization problems. Inspired by the potential of NFSA, this paper proposes an evolutionary multi-objective optimizatio...

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Vydáno v:Engineering with computers Ročník 38; číslo 3; s. 2527 - 2548
Hlavní autoři: Tan, Weng-Hooi, Mohamad-Saleh, Junita
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.06.2022
Springer Nature B.V
Témata:
Algorithms
Approximation
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Evolutionary algorithms
Indicators
Math. Applications in Chemistry
Mathematical analysis
Mathematical and Computational Engineering
Multiple objective analysis
Optimization
Original Article
Performance evaluation
Performance measurement
Systems Theory
Pareto front
Multi-objective optimization
Performance metric
Fish swarm algorithm
Unconstrained test function
ISSN:0177-0667, 1435-5663
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Shrnutí:Normative Fish Swarm Algorithm (NFSA) is a novel variant of the Artificial Fish Swarm Algorithm (AFSA) proposed in 2019, and has been proven effective in solving single-objective optimization problems. Inspired by the potential of NFSA, this paper proposes an evolutionary multi-objective optimization algorithm, namely “Multi-Objective Normative Fish Swarm Algorithm (MO-NFSA)”. However, due to the fact that NFSA was originally modeled for single-objective optimization, a certain degree of transformation, modification, and additional strategies (related to multi-objective optimization features) must be integrated into the original NFSA as part of MO-NFSA modeling for further extension. This article adopts a total of 15 multi-objective optimization test cases in any category of fixed-dimensional, non-fixed-dimensional (ZDT set) or scalable multi-objective (DTLZ set) optimization types. These multi-objective optimization test cases are used to compare the performance of MO-NFSA with other comparative algorithms. However, performance evaluation of multi-objective optimization is a daunting task. Their performance results can only be digitized through quality indicators (i.e., performance metrics), which are mainly tested from three different aspects of high-quality approximation: convergence, uniformity, and spread. Here, quality indicators including generation distance (GD), spacing (S), and spread-delta (∆/∆*) are used to check the quality performance of each algorithm based on the corresponding approximation. In the work, 20 simulations are run, and hence, 20 sets of data are collected. The collected results prove that MO-NFSA is superior to other comparison algorithms in all aspects of high-quality approximation. MO-NFSA can solve different types of multi-objective optimization problems.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-020-01223-4