A heuristic algorithm enhanced with probability-based incremental learning and local search for dynamic facility layout problems

Pradeepmon, T.G.; Panicker, Vinay V.; Sridharan, R.

doi:10.1504/ijads.2018.095279

Cited by 3 publications

(2 citation statements)

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“…Genetic algorithms (GAs) [4,5] are based on the gradient-free approach that mimics evolution. Since then, several meta-heuristic nature-inspirated techniques have been formulated; these include particle swarm optimization (PSO) [6][7][8], evolutionary strategy (ES) [9], firefly algorithm (FA) [10], ant colony optimization (ACO) [11], differential evolution (DE) [12], probability-based incremental learning (PBIL) [13], big bang-big crunch algorithm [14], biogeography-based optimization (BBO) [15], harmony search (HS) [16], cuckoo search (CS) [17], animal migration optimization (AMO) [18], krill herd method (KH) [19], bat algorithm (BA) [20], teachinglearning-based optimization (TLBO) [21], and charged system search (CSS) [22]. KH is a modern swarm intelligence optimization technique inspired by krill herding behaviour [23].…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Krill Herd Optimization Algorithm for Global Numerical Estimation of the Attraction Domain for Nonlinear Systems

et al. 2021

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Nowadays, solving constrained engineering problems related to optimization approaches is an attractive research topic. The chaotic krill herd approach is considered as one of most advanced optimization techniques. An advanced hybrid technique is exploited in this paper to solve the challenging problem of estimating the largest domain of attraction for nonlinear systems. Indeed, an intelligent methodology for the estimation of the largest stable equilibrium domain of attraction established on quadratic Lyapunov functions is developed. The designed technique aims at computing and characterizing a largest level set of a Lyapunov function that is included in a particular region, satisfying some hard and delicate algebraic constraints. The formulated optimization problem searches to solve a tangency constraint between the LF derivative sign and constraints on the level sets. Such formulation avoids possible dummy solutions for the nonlinear optimization solver. The analytical development of the solution exploits the Chebyshev chaotic map function that ensures high search space capabilities. The accuracy and efficiency of the chaotic krill herd technique has been evaluated by benchmark models of nonlinear systems. The optimization solution shows that the chaotic krill herd approach is effective in determining the largest estimate of the attraction domain. Moreover, since global optimality is needed for proper estimation, a bound type meta-heuristic optimization solver is implemented. In contrast to existing strategies, the synthesized technique can be exploited for both rational and polynomial Lyapunov functions. Moreover, it permits the exploitation of a chaotic operative optimization algorithm which guarantees converging to an expanded domain of attraction in an essentially restricted running time. The synthesized methodology is discussed, with several examples to illustrate the advantageous aspects of the designed approach.

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Section: Introductionmentioning

confidence: 99%

A Chaotic Krill Herd Optimization Algorithm for Global Numerical Estimation of the Attraction Domain for Nonlinear Systems

et al. 2021

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“…Genetic algorithm (GA) [5] is the most popular method, which utilizes the Darwinian evolution theory from the natural evolution-inspired methods. Other algorithms such as biogeography-based optimizer (BBO) [6], genetic programming (GP) [7], and probability-based incremental learning (PBIL) [8] are examples of biology-based methods.…”

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confidence: 99%