Lifecycle-Based Swarm Optimization for Constrained Problem of Engineering

Abstract

There are many constrained optimization problems in engineering. Bio-inspired optimization algorithms have been widely used to solve various engineering problems. This paper presents a novel optimization algorithm called Lifecycle-based Swarm Optimization, inspired by biology life cycle. LSO algorithm imitates biologic life cycle 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 a Vehicle Routing Problem with Time Windows for demonstration the effectiveness and stability. The results demonstrate remarkable performance of the LSO algorithm on chosen case when compared to two successful optimization techniques.

Lifecycle-based swarm optimization method for multi-objective optimization problem (MOP)

“…Then 7 unimodal unconstrained optimization test functions, and constrained optimization test functions, and engineering problems included Vehicle Routing Problem (VRP) problem and Vehicle Routing Problem with Time Windows (VRPTW) problem, were adopt to test LSO algorithm performance [1][2][3]. Above experiments demonstrate that LSO is a competitive and effective approach.…”

Lifecycle-Based Swarm Optimization Method for Numerical Optimization

“…In 2011, borrowing the biologic lifecycle theory, the Lifecycle-based swarm optimization (LSO) algorithm was proposed for the first time [24]. Then, 7 unimodal unconstrained optimization test functions and constrained optimization test functions as well as engineering problems that include vehicle routing problem (VRP) and vehicle routing problem with Time Windows (VRPTW) were adopted to test LSO algorithm performance [24][25][26]. The above experiments demonstrate that LSO is a competitive and effective approach.…”