A Memetic Algorithm for Mixed-Integer Optimization Problems

• Published in: Applied Mechanics and Materials Vol. 284-287; pp. 2970 - 2974
• Main Author: Lin, Yung Chien
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 25.01.2013
• Subjects: Evolutionary Algorithm (EA); Memetic Algorithm; Mixed-Integer Optimization
• ISBN: 3037856122; 9783037856123
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.284-287.2970

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on a benchmark mixed-integer constrained optimization problem. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.