A Complementary Cyber Swarm Algorithm

Abstract

A recent study (Yin et al., 2010) Standard PSO 2007 method (Clerc, 2008. showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the tabu search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to thewere previously found as a basis for focusing the search in regions anticipated to harbor additional solutions of high quality. Diversification promotes the exploration of regions appreciably different from those previously examined in order to produce new solutions with characteristics that depart from those already seen. Intensification and diversification work together to identify new promising regions when the slave heuristics stagnate in the executed search courses. Many intelligent algorithms fall in the territory of metaheuristics. DOI: 10.4018/jsir.2011040102 International Journal of Swarm Intelligence Research, 2(2), 22-41, April-June 2011 23 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.Some exemplary algorithms (Luke, 2009) are genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), tabu search (TS), particle swarm optimization (PSO), scatter search (SS), greedy randomized adaptive search procedure (GRASP), variable neighborhood search (VNS), to name a few. A recent survey and descriptive analysis of metaheuristic algorithms can be found in Sorensen and Glover (2010).Slave heuristics embedded in metaheuristic methods often adopt solution combination or neighborhood exploration processes to generate new solutions based on the current state of search. Solution combination approaches produce new solutions by exchanging information between candidate solutions (for example, crossover operation executed in GA) or by using candidate solutions as guiding points for producing new solutions (for example, by reference to the best experiences in PSO or the path relinking (PR) process used in SS). Alternatively, neighborhood exploration employs incremental changes, called ...

A Complementary Cyber Swarm Algorithm

“…The Cyber Swarm Algorithm (CyberSA) of Yin et al (2010) creates an enhanced form of swarm algorithms by incorporating three features: (1) augmenting the information sharing among particles by learning from the reference set members, (2) systematically generating dynamic social networks in order to choose various solutions as the leaders such that the search can adapt to different functional landscape, and (3) executing diversification strategies based on path relinking approaches as a response to the status of the adaptive memory. The success of this method has motivated us to examine another variant that draws on alternative ideas from the same sources.…”

“…As previously noted, the choice of neighborhood topologies and leading solutions significantly affects the particle swarm performance. The literature discloses that the use of a dynamic neighborhood (Miranda et al, 2007;Clerc, 2008;Yin et al, 2010) and the local best solution lbest (Kennedy, 1999;Clerc, 2008) leads to a better performance. These notions create a form of multiple neighborhood search in which the neighboring particles (each maintaining a search trajectory) are selected at random or systematically and the local optimum corresponds to the best solution encountered by the multiple search trajectories.…”

“…In additional to the original mesh search, the particle swarm search can explore the nonconvexity of the objective function. More recently, Yin et al (2010) introduced a Cyber Swarm Algorithm (CyberSA) as the marriage of PSO and the scatter search/path relinking (SS/PR) template that obtains improved outcomes. With the addition of an external memory, embodied in a reference set consisting of the best solutions observed throughout the evolution history, useful information is produced and maintained that is not attainable by relying on traditional PSO mechanisms involving particle experiences.…”

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A recent study (Yin et al, 2010) showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the tabu search (TS) and the SS/PR template.

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A Hybrid Ant Colony Algorithm for Quadratic Assignment Problem

“…It has advantages such as parallel and high solving precision. Particle swarm optimization (PSO) is firstly intended for simulating social behaviors that a bird flock search for foods and it is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality [10]. It is suitable to find the optimal combination of parameters in many algorithms and further optimize solutions searched by these algorithms.…”