Many-objective BAT algorithm

PloS One
Uzman PerwaizAdeem Ali Anwar

Abstract

In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions a...Continue Reading

References

May 13, 1983·Science·S KirkpatrickM P Vecchi
Sep 5, 2014·TheScientificWorldJournal·Iztok FisterIztok Fister
Nov 1, 2017·Computational Intelligence and Neuroscience·Xiguang LiXiaojing Liu

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Citations

Aug 17, 2021·Soft Computing·Hafiz Tayyab RaufMd Tabrez Nafis

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Software Mentioned

NSGA
MOPSOs
MaOP
PSO
MOEA
MOBAT
MaOBAT
MOEAs
III
MaOPs

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