Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms. However, the PSO converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. To improve the diversity of the PSO, we here propose a memetic algorithm called particle swarm gravitation optimization (PSGO). After a specific number of iterations, some individuals selected from the PSO and GSA systems are exchanged by the roulette wheel approach. Finally, to increase the diversities of the PSO and GSA, we introduce a diversity enhancement operator, which is inspired by the crossover operator used in differential evolution algorithms. In evaluations of five benchmark functions, the PSGO significantly outperformed the PSO and Cuckoo search and yielded a superior performance to the GSA of most of instances and computation times. 8 9 10 11 12 13 14 15 16 17 19 rithms [1] have been proposed, including ant colony 20 optimization (ACO) [2], particle swarm optimiza-21 tion (PSO) [3], artificial bee colony (ABC) [4], the 22 firefly algorithm (FA) [5], and Cuckoo search (CS) 23 [6]. These algorithms have been applied to diverse 24 real-world problems such as clustering [7], the travel-25 ing salesman [8], job-shop scheduling [9], flow-shop 26 scheduling [10], feature selection [11], DNA frag-27 ment Assembly [12], and even the Water reactor 28 problem [13]. 29 The most famous of the above mentioned nature-30 inspired algorithms is PSO algorithm. However, the 31 * PSO is prone to premature convergence, especially 32 when approaching local optima that are difficult to 33 escape [14]. Thus, maintaining sufficiently high par-34 ticle diversity to avoid premature convergence is an 35 important task. Researchers have proposed several 36 algorithms that improve the exploration and exploita-37 tion of the PSO, such as CLPSO [15], OLPSO [16], and 38 DNSPSO [17]. 39 A recently proposed swarm-based intelligence algo-40 rithm is the gravitation search algorithm (GSA) [18]. 41 GSA is based on the Newtonian gravity, and the search 42 space is calculated based on the overall force obtained 43 by all individuals. The GSA is based on Newtonian 44 gravity, and the search space depends on the overall 45 force exerted by all individuals. Inspired by the search 46 strategy of GSA and the fast convergence of PSO, we gravitation optimization (PSGO) algorithm. The pro-49 posed PSGO algorithm simultaneously executes PSO 50 1064-1246/15/$35.00 © 2015 -IOS Press and the authors. All rights reserved U n c o r r e c t e d A u t h o r P r o o f 2 K.-W Huang et al. / PSGO: Particle Swarm Gravitation Optimization Algorithm and GSA and adopts the center PSO (CPSO) [19] 51 concept to increase the likelihood that the solution 52 approaches the global optimum. After a specific number 53 of iterations, some individuals are exchanged between 54 the PSO and GSA systems using a roulette wheel 55 approach [20]. Finally, the diversity of PSGO is fine-56 tuned by coupling to a diversity enhancement operator 57 that ...

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