Abstract
Aiming at solving the drawbacks of the original binary ant colony algorithm on multi-objective optimization problems: easy to fall into the local optimization and difficult to get the Pareto optimal solutions, we proposed Multi-population Binary ant Colony Algorithm with Concrete Behaviors (MPBACB). The algorithm introduced multi-population method to ensure the globe optimization ability, and use environmental evaluation/reward model to improve the searching efficiency. Furthermore, concrete ant behaviors are defined to stabilize the performance of algorithm. The experimental results on several constrained multi-objective functions prove that the algorithm ensure the good global search ability, and has better effect to the multi-objective problems.
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