Genetic Algorithm for Multi-choice Integer Linear Programming Problems

Mohanty, Dipana Jyoti; Jana, Rabin K.; Biswal, M. P.

doi:10.1007/978-981-13-1592-3_64

Cited by 4 publications

(1 citation statement)

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“…Evolutionary algorithms (EAs), including differential evolution (DE) [1][2][3], particle swarm optimization (PSO) [4,5], and genetic algorithm (GA) [6,7], have demonstrated remarkable search performance in numerous practical engineering problems [8], such as optimization design of internal combustion engine [9] and building designs [10]. However, real-world engineering problems often involve complex constraints, which are referred as constrained optimization problems (COPs).…”

Section: Introductionmentioning

confidence: 99%

Population state-driven surrogate-assisted differential evolution for expensive constrained optimization problems with mixed-integer variables

Liu,

Yuan,

Yang

et al. 2024

Complex Intell. Syst.

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Many surrogate-assisted evolutionary algorithms (SAEAs) have been shown excellent search performance in solving expensive constrained optimization problems (ECOPs) with continuous variables, but few of them focus on ECOPs with mixed-integer variables (ECOPs-MI). Hence, a population state-driven surrogate-assisted differential evolution algorithm (PSSADE) is proposed for solving ECOPs-MI, in which the adaptive population update mechanism (APUM) and the collaborative framework of global and local surrogate-assisted search (CFGLS) are combined effectively. In CFGLS, a probability-driven mixed-integer mutation (PMIU) is incorporated into the classical global DE/rand/2 and local DE/best/2 for improving the diversity and potentials of candidate solutions, respectively, and the collaborative framework further integrates both the superiority of global and local mutation for the purpose of achieving a good balance between exploration and exploitation. Moreover, the current population is adaptively reselected based on the efficient non-dominated sorting technique in APUM when the population distribution is too dense. Empirical studies on 10 benchmark problems and 2 numerical engineering cases demonstrate that the PSSADE shows a more competitive performance than the existing state-of-the-art algorithms. More importantly, PSSADE provides excellent performance in the design of infrared stealth material film.

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