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.
Highlights
Modern engineering tries to solve classical problems by combining new trends on technologies with traditional models
In order to evaluate the performance of the autonomous algorithm, we have used two sets of the machine-part cell formation, the first consisting of 90 instances proposed by Boctor [27], while the second consists of 70 new instances which have a higher level of complexity
We have presented an adaptive procedure that allows for identifying a stagnation in a local optimal
Summary
Modern engineering tries to solve classical problems by combining new trends on technologies with traditional models. A crucial step for building these competitive systems is the machine-part cell formation (MPCF), which involves the decomposition of a manufacturing industry in completely independent clusters called cells. Those cells contain machines that process parts that share some features or belong to the same family. A universally optimal parameter value set for a given metaheuristic does not exist [25] Based on this approach, the improved dolphin echolocation algorithm is able to autonomously adjust its population at runtime, according to its exhibited performance. The experimental results, statistical comparisons of results, and conclusions of this work are shown in Sections 6 and 7, respectively
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