Influence of adjusting Big Bang-Big Crunch Algorithm's parameters on resolving Constraint-based Optimization problems

Abstract

Metaheuristic is a collection of algorithms that are strongly discussed and used in the literature. Typically metaheuristics algorithm uses stochastic operators that makes each iteration unique, and often haves controlling parameters that have an  impact on convergence process astheir effects mostly ignored in most literature related to optimization, which make it difficult to draw a conclusion. The Big Bang–Big Crunch (BB-BC) metaheuristic algorithm was introduced in this work to evaluate the performance of a metaheuristic algorithm in relation to its control parameter. And it shows the effects of variety in the values of BB-BC in solving A well-known engineering optimization problem known as“ tension/ compression spring design problem” that’s considered as a Single-Objective Constrained Optimization Problems.As part of the Experimental results, multiple beginning parameters for the BB-BC are tested. This is done in an attempt to discover the algorithm's optimum beginning parameters. And the minimum, maximum, and mean results of the penalized objectives functions are then computed.