An algorithm inspired by social spiders for truss optimisation problems. Issue 8 (6th November 2017)

• Record Type: Journal Article
• Authors: Duarte, Grasiele Regina; Lemonge, Afonso Celso de Castro; da Fonseca, Leonardo Goliatt
• Is Part Of: Engineering computations. Volume 34:Issue 8(2017)
• Page Start: 2767
• Page End: 2792
• Publication Date: 2017-11-06
• Subjects: Structural optimization -- Constraint handling -- Social spider algorithm; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-12-2016-0447
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The purpose of this paper is to evaluate the performance of social spider algorithm (SSA) to solve constrained structural optimisation problems and to compare its results with others algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony. Design/methodology/approach: To handle the constraints of the problems, this paper couples to the SSA an efficient selection criteria proposed in the literature that promotes a tournament between two solutions in which the feasible or less infeasible solution wins. The discussion is conducted on the competitiveness of the SSA with other algorithms as well as its performance in constrained problems. Findings: SSA is a population algorithm proposed for global optimisation inspired by the foraging of social spiders. A spider moves on the web towards the position of the prey, guided by vibrations that occur around it in different frequencies. The SSA was proposed to solve problems without constraints, but these are present in most of practical problems. This paper evaluates the performance of SSA to solve constrained structural optimisation problems and compares its results with other algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony. Research limitations/implications: The proposed algorithm has no limitations, and it can be applied in other classes of constrained optimisation problems. Practical implications: This paper evaluated the proposed algorithm with a benchmark of constrained structural optimisation problems intensely used in the literature, but it can be applied to solve real constrained optimisation problems in engineering and others areas. Originality/value: This is the first paper to evaluate the performance of SSA in constrained problems and to compare its results with other algorithms traditional in the literature.