—Each biologic must go through a process from birth, growth, reproduction until death, this process known as life cycle. This paper borrows the biologic life cycle theory to propose a Lifecycle-based Swarm Optimization (LSO) algorithm. Based on some features of life cycle, LSO designs six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. In this paper, the capability of the LSO to address constrained optimization problem was investigated. Firstly, the proposed method was test on some well-known and widely used benchmark problems. When compared with PSO, we can see that LSO can obtain the better solution and lower standard deviation than PSO on many different types of constrained optimization problems. Finally, LSO was also used for seeking the optimal route for vehicle route problem in logistics system. The result of LSO is the best when comparing with PSO and GA. The results of above two types of experiments, which include not only the ordinary benchmark problem but also the practical problems in engineering, demonstrate that LSO is a competitive and effective approach for solving constrained problems.
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