Arasomwan Martins Akugbe, Adewumi Aderemi Oluyinka
School of Mathematics, Statistics, and Computer Science, University of Kwazulu-Natal South Africa, Private Bag X54001, Durban 4000, South Africa.
ScientificWorldJournal. 2014 Feb 25;2014:798129. doi: 10.1155/2014/798129. eCollection 2014.
A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants.
提出了一种新的局部搜索技术，并通过解决早熟收敛问题来提高粒子群优化算法的性能。在所提出的局部搜索技术中，解搜索空间中的一个潜在粒子位置由群体中的多个随机选择的粒子共同构建。选择的次数随优化问题的维度而变化，每个选定的粒子从其个人最佳位置贡献其随机选择维度位置处的值。构建潜在粒子位置后，与当前群体全局最佳位置相比，在其邻域周围进行一些局部搜索。如果发现它更好，则用它替换全局最佳粒子位置；否则不进行替换。使用一些经过充分研究的低维和高维基准问题，通过数值模拟来验证改进算法的性能。与四种不同的粒子群优化变体进行了比较，其中两种变体实现了不同的局部搜索技术，而另外两种则没有。结果表明，改进算法能够获得质量更好的解，同时在收敛速度、精度、稳定性、鲁棒性以及全局-局部搜索能力方面比竞争变体表现更好。
