Biogeography-based optimization for constrained optimization problems

• Published in: Computers & operations research Vol. 39; no. 12; pp. 3293 - 3304
• Main Authors: Boussaïd, Ilhem, Chatterjee, Amitava, Siarry, Patrick, Ahmed-Nacer, Mohamed
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.12.2012; Elsevier; Pergamon Press Inc
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Biogeography; Biogeography-based optimization; Computer Science; Computer science; control theory; systems; Constrained optimization; Constraints; Exact sciences and technology; Inequalities; Mathematical analysis; Mathematical models; Mathematical problems; Mathematical programming; Operational research and scientific management; Operational research. Management science; Operations Research; Optimization; Optimization algorithms; Preserves; Selection procedure; Stochastic models; Stochasticity; Strategy; Studies; Theoretical computing; Selection procedure; Biogeography-based optimization; Constrained optimization; Unconstrained optimization; Evolutionary algorithm; Expert system; Inequality constraint; Equality constraint; Biogeography; Feasibility; Objective function; Stochastic programming
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2012.04.012

Biogeography-based optimization (BBO) has been recently proposed as a viable stochastic optimization algorithm and it has so far been successfully applied in a variety of fields, especially for unconstrained optimization problems. The present paper shows how BBO can be applied for constrained optimization problems, where the objective is to find a solution for a given objective function, subject to both inequality and equality constraints. To solve such problems, the present work proposes three new variations of BBO. Each new version uses different update strategies, and each is tested on several benchmark functions. A successful implementation of an additional selection procedure is also proposed in this work which is based on the feasibility-based rule to preserve fitter individuals for subsequent generations. Our extensive experimentations successfully demonstrate the usefulness of all these modifications proposed for the BBO algorithm that can be suitably applied for solving different types of constrained optimization problems.