多目标自适应和声搜索算法

Multi-objective self-adaptive harmony search algorithm

提出了一种利用Pareto支配来求解多目标优化问题的自适应和声搜索算法(MOSAHS)。该算法利用外部种群来保存非支配解,为了保持非支配解的多样性,提出了一种基于拥挤度的删除策略,这个策略能较好地度量个体的拥挤程度。用5个标准测试函数对其进行测试,并与其他多目标优化算法相比较。实验结果表明,与其他的算法相比,提出的算法在逼近性和均匀性两方面都有很好的表现,是一种有效的多目标和声搜索算法。

A self-adaptive harmony search algorithm incorporate Pareto dominance to solve multi-objective optimization problems is presented.The algorithm adopts an external archive to keep non-dominated solutions.In order to maintain the diversity of the non-dominated solutions,a crowding measure is proposed in this article.The crowding strategy can measure the crowding degree accurately.The experiments are performed using five benchmark test functions and compared with other multi-objective optimization algorithms.The experiment results show that,the proposed MOSAHS algorithm is an effective multi-objective harmony search algorithm with fine performance in both convergence and diversity.