A novel particle swarm optimization algorithm based on particle migration

Inspired by the migratory behavior in the nature, a novel particle swarm optimization algorithm based on particle migration (MPSO) is proposed in this work. In this new algorithm, the population is randomly partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization with time varying inertia weight and acceleration coefficients (LPSO-TVAC). At periodic stage in the evolution, some particles migrate from one complex to another to enhance the diversity of the population and avoid premature convergence. It further improves the ability of exploration and exploitation. Simulations for benchmark test functions illustrate that the proposed algorithm possesses better ability to find the global optima than other variants and is an effective global optimization tool. Particle swarm optimization (PSO) is a population-based stochastic, heuristic optimization algorithm with inherent par-allelism, firstly introduced in 1995 by Kennedy and Eberhart [1,2]. It is a member of the wild category of swarm intelligence, and draw inspiration from the simplified animal social behaviors, such as bird flocking, fish schooling, etc. In the PSO, each individual is treated as a volume-less point, which referred to as particle in the multidimensional search space. The population is called as swarm, and the trajectory of each particle in the search space is adjusted by dynamically altering its velocity. These particles fly through problem space and have two essential reasoning capabilities: their memory of their own best position and knowledge of the global or their neighborhood's best. Since the introduction of PSO, it has attracted comprehensive attention due to its effectiveness and robustness in the global optimization research field, as well as simplicity of implementation [3,4]. As a swarm intelligence algorithm, some researchers have noted a tendency for the swarm to converge prematurely on local optima [5], especially in complex multi-peak-search problems. In view of the shortcoming of the standard PSO algorithm, a few variants of the algorithm have been suggested through empirical simulations over the past decade, some have resulted in improved general performance, and some have improved performance on particular kinds of problems. These variants can be classified into several groups as: parameter selecting [6,7], integration of its self-adaptation [8–15], evolution strategy [16–22] and integrating with other intelligent optimizing methods [23–26]. For a detailed review of the particle swarm optimization and its different variants, readers are encouraged to refer to the review articles written by Eberhart and Shi [3] and Poli et al. [5]. …