Ecosystem particle swarm optimization
 First Online:
DOI: 10.1007/s0050001621114
 Cite this article as:
 Liu, J., Ma, D., Ma, T. et al. Soft Comput (2017) 21: 1667. doi:10.1007/s0050001621114
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Abstract
Particle swarm optimization (PSO) is a wellknown swarm intelligence algorithm inspired by the foraging behavior of bird flocking. PSO has been widely used in many optimization and engineering problems due to its simplicity and efficiency, even though there still exist some disadvantages. The standard PSO often suffers with premature convergence or slow convergence when the optimization problem is multimodal or highdimensional. To overcome these drawbacks, an ecosystem PSO (ESPSO) inspired by the characteristic that a natural ecosystem can excellently keep the biological diversity and make the whole ecosystem be in a dynamic balance is presented in this paper. ESPSO not only prevents the algorithm trapping into local optima but also balances the exploration and exploitation in both unimodal and multimodal problems as compared to other PSO variants. Twenty benchmark functions including unimodal functions and multimodal nonlinear functions are used to test the searching ability of ESPSO. Experimental results show that ESPSO considerably improves the searching accuracy, the algorithm reliability and the searching efficiency in comparison with other six wellknown PSO variants and four evolutionary algorithms. Moreover, ESPSO was successfully applied to the antenna array pattern synthesis design and gained satisfactory results.
Keywords
Particle swarm optimization Ecosystem mechanism Antenna array pattern synthesisFunding information
Funder Name  Grant Number  Funding Note 

National Nature Science Foundation of China 
