Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A heuristic particle swarm optimizer for optimization of pin connected structures
Computers and Structures
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
A Novel Hybrid Real-Valued Genetic Algorithm for Optimization Problems
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
A Hybrid Optimization Method for Fuzzy Classification Systems
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Self-adaptive harmony search algorithm for optimization
Expert Systems with Applications: An International Journal
Computer Aided Systems Theory - EUROCAST 2009
Harmony search in water pump switching problem
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An improved adaptive binary Harmony Search algorithm
Information Sciences: an International Journal
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Harmony Search (HS) is an emerging meta-heuristic optimization method and has been used to tackle various optimization problems successfully. However, the research of multi-objectives HS just begins and no work on binary multi-objectives HS has been reported. This paper presents a multi-objective binary harmony search algorithm (MBHS) for tackling binary-coded multiobjective optimization problems. A modified pitch adjustment operator is used to improve the search ability of MBHS. In addition, the non-dominated sorting based crowding distance is adopted to evaluate the solution and update the harmony memory to maintain the diversity of algorithm. Finally the performance of the proposed MBHS was compared with NSGA-II on multi-objective benchmark functions. The experimental results show that MBHS outperform NSGA-II in terms of the convergence metric and the diversity metric.