A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

Ricardo Soto, Broderick Crawford, Rodrigo Olivares, César Carrasco, Eduardo Rodriguez-Tello, Carlos Castro, Fernando Paredes and Hanns de la Fuente-Mella
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Ricardo Soto: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
Broderick Crawford: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
Rodrigo Olivares: Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile
César Carrasco: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
Eduardo Rodriguez-Tello: Cinvestav Tamaulipas, Km. 5.5 Carretera Victoria - Soto La Marina, Victoria Tamps. 87130, Mexico
Carlos Castro: Departamento de Informática, Universidad Técnica Federico Santa Maria, Valparaíso 2390123, Chile
Fernando Paredes: Escuela de Ingeniería Industrial, Universidad Diego Portales, Santiago 8370109, Chile
Hanns de la Fuente-Mella: Escuela de Comercio, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile

Mathematics, 2020, vol. 8, issue 9, 1-25

Abstract: In this paper, we integrate the autonomous search paradigm on a swarm intelligence algorithm in order to incorporate the auto-adjust capability on parameter values during the run. We propose an independent procedure that begins to work when it detects a stagnation in a local optimum, and it can be applied to any population-based algorithms. For that, we employ the autonomous search technique which allows solvers to automatically re-configure its solving parameters for enhancing the process when poor performances are detected. This feature is dramatically crucial when swarm intelligence methods are developed and tested. Finding the best parameter values that generate the best results is known as an optimization problem itself. For that, we evaluate the behavior of the population size to autonomously be adapted and controlled during the solving time according to the requirements of the problem. The proposal is testing on the dolphin echolocation algorithm which is a recent swarm intelligence algorithm based on the dolphin feature to navigate underwater and identify prey. As an optimization problem to solve, we test a machine-part cell formation problem which is a widely used technique for improving production flexibility, efficiency, and cost reduction in the manufacturing industry decomposing a manufacturing plant in a set of clusters called cells. The goal is to design a cell layout in such a way that the need for moving parts from one cell to another is minimized. Using statistical non-parametric tests, we demonstrate that the proposed approach efficiently solves 160 well-known cell manufacturing instances outperforming the classic optimization algorithm as well as other approaches reported in the literature, while keeping excellent robustness levels.

Keywords: autonomous search; swarm intelligence; auto-adjust parameter values; cell manufacturing systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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