Abstract
Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO). Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.
Highlights
In nature, biology species are divers and an organism is any living thing [1]
This paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO)
In order to verify the efficiency of our approach to settle practical problem and test the goodness of Lifecycle-based swarm optimization (LSO), the mechanical design optimization problem was selected as the testing case, which included pressure vessel and schematic diagram of welded beam problem
Summary
Biology species are divers and an organism is any living thing (such as animal, plant, or microorganism) [1]. All their behaviors can show what kind of biological features they have. With the purpose of solving reality complex problem, researchers begin to mimic the biologic phenomena via defining a set of rules and realize those rules on computer [3]. Those rules are called bioinspired optimization technique
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