A Differential Evolution Algorithm for Optimization Including Linear Equality Constraints

• Published in: Progress in Artificial Intelligence Vol. 9273; pp. 262 - 273
• Main Authors: Barbosa, Helio J. C., Araujo, Rodrigo L., Bernardino, Heder S.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: 3D graphics & modelling; Artificial intelligence; Candidate Solution; Continuous Optimization Problem; Feasible Direction; Information retrieval; Objective Function Evaluation; Particle Swarm Optimization
• ISBN: 9783319234847; 3319234846
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-23485-4_26

In this paper a differential evolution technique is proposed in order to tackle continuous optimization problems subject to a set of linear equality constraints, in addition to general non-linear equality and inequality constraints. The idea is to exactly satisfy the linear equality constraints, while the remaining constraints can be dealt with via standard constraint handling techniques for metaheuristics. A procedure is proposed in order to generate a random initial population which is feasible with respect to the linear equality constraints. Then a mutation scheme that maintains such feasibility is defined. The procedure is applied to test-problems from the literature and its performance is also compared with the case where the constraints are handled via a selection scheme or an adaptive penalty technique.