Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Numerical Comparison of Some Penalty-Based Constraint Handling Techniques in Genetic Algorithms
Journal of Global Optimization
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Society and civilization: An optimization algorithm based on the simulation of social behavior
IEEE Transactions on Evolutionary Computation
Guided artificial bee colony algorithm
ECC'11 Proceedings of the 5th European conference on European computing conference
An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems
Journal of Intelligent Manufacturing
International Journal of Metaheuristics
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We propose an evolutionary-based approach to solve engineering design problems without using penalty functions. The aim is to identify and maintain infeasible solutions close to the feasible region located in promising areas. In this way, using the genetic operators, more solutions will be generated inside the feasible region and also near its boundaries. As a result, the feasible region will be sampled well-enough as to reach better feasible solutions. The proposed approach, which is simple to implement, is tested with respect to typical penalty function techniques as well as against state-of-the-art approaches using four mechanical design problems. The results obtained are discussed and some conclusions are provided.