A Hybrid Harmony Search Algorithm Combined with Differential Evolution for Global Optimization Problems
Harmony search (HS) is a recently proposed meta-heuristic by imitating music improvisation process, which has drawn much attention in the past few years. However, researches have revealed that the performance and the convergence rate of the method are suffered when dealing with high-dimensional or/and multimodal problems. To get a better control between exploitation and exploration, a hybrid HS algorithm is proposed, which is characterized in two aspects. First, the memory consideration scheme is modified by introducing crossover and mutation operators, which is inspired by the differential evolution (DE) algorithm. Second, two control parameters, namely PAR and bw, are either dynamically adjusted or self-learning along with the evolution process to fine-tune the solutions. Numerical results based on a test suite of well-known benchmark functions show that the proposed algorithm is more effective or at least competitive in finding near-optimal solutions compared with three HS variants and the DE/rand/1/bin algorithm.
Harmony search continuous optimization meta-heuristic differential evolution mutation
CHEN Jing WANG Ya-min LI Jun-qing
College of Computer Science, Liaocheng University, Liaocheng, 252059, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
2509-2513
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）