科研之友
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摘要
Many real-world problems belong to the family of discrete optimization problems. Most of these problems are NP-hard and difficult to solve efficiently using classical linear and convex optimization methods. In addition, the computational difficulties of these optimization tasks increase rapidly with the increasing number of decision variables. A further difficulty can be also caused by the search space being intrinsically multimodal and non-convex. In such a case, it is more desirable to have an effective optimization method that can cope better with these problem characteristics. Binary particle swarm optimization (BPSO) is a simple and effective discrete optimization method. The original BPSO and its variants have been used to solve a number of classic discrete optimization problems. However, it is reported that the original BPSO and its variants are unable to provide satisfactory results due to the use of inappropriate transfer functions. More specifically, these transfer functions are unable to provide BPSO a good balance between exploration and exploitation in the search space, limiting their performances. To overcome this problem, this paper proposes to employ a time-varying transfer function in the BPSO, namely TVT-BPSO. To understand the search behaviour of the TVT-BPSO, we provide a systematic analysis of its exploration and exploitation capability. Our experimental results demonstrate that TVT-BPSO outperforms existing BPSO variants on both low-dimensional and high-dimensional classical 0-1 knapsack problems, as well as a 200-member truss problem, suggesting that TVT-BPSO is able to better scale to high dimensional combinatorial problems than the existing BPSO variants and other metaheuristic algorithms.
- 出版日期2017-10
