Spread enhancement for firefly algorithm with application to control mechanism of exoskeleton system

Kasdirin, Hyreil Anuar; Ali, Siti Khadijah; Tokhi, M. O.

doi:10.15282/mekatronika.v1i1.163

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…SI algorithms, including artificial bee colony (ABC) [1] and bat algorithm (BA) [2], are motivated by the cooperative behavior of animals, while the EAs, such as genetic algorithm (GA) [3] and differential evolution (DE) [4], are inspired by the Darwin's Theory of Evolution. MSAs are widely implemented to solve various type of optimization problems with different complexity level [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], due to its strengths of fast convergence speed and promising global search ability.…”

Section: Introductionmentioning

confidence: 99%

Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems

et al. 2021

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Different variants of particle swarm optimization (PSO) algorithms were introduced in recent years with various improvements to tackle different types of optimization problems more robustly. However, the conventional initialization scheme tends to generate an initial population with relatively inferior solution due to the random guess mechanism. In this paper, a PSO variant known as modified PSO with chaotic initialization scheme is introduced to solve unconstrained global optimization problems more effectively, by generating a more promising initial population. Experimental studies are conducted to assess and compare the optimization performance of the proposed algorithm with four existing well-establised PSO variants using seven test functions. The proposed algorithm is observed to outperform its competitors in solving the selected test problems.

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