FSGSO Case Study

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Oliveira, Pacifico and Ludermir [15] are proposed Fish Swarm Group Search Optimizer (FSGSO) which is a hybrid Group Search Optimization based on the behaviors of fish swarms. The hybridization is based on the scrounging procedures in which were used the behaviors of Swarm, Follow, Prey and Leap from the Artificial Fish Swarm Algorithm [16]. In FSGSO, the producer maintains its search procedures, but the scroungers will execute the search procedures of fish. In traditional GSO scrounging strategies such as the area copying (common scrounging behavior), which can be modeled as a random walk towards the producer. But in FSGSO the scroungers could take into consideration the producer, the center of the group and also random walks. In this method, the scrounging is performed through the execution of two behaviors from the AFSA: Swarm and Follow. The follow behavior represents the ability to
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In the Swarm behavior the update process in case the conditions (the region is not crowded (nf/N<σ) and the central position is in a better food concentration region than the current fish ( yc>yi)…show more content…
D. S. Pacifico and T. B. Ludermir[17] are introduce a novel GSO approach based on cooperative behavior among groups, called Cooperative Group Search Optimizer (CGSO). The cooperative behavior is obtained by a divide-and-conquer strategy, where each group is responsible for a limited set of the problem variables, and the final solution is found by combining solutions found by each group. In CGSO, the population members are divided in k independent groups. Each group is associated with d dimensions from the search space (where d x k = n). Each group will execute local searches seeing to minimize its own set of variables. The current fitness of each member from this group by combining its variables with the best solutions found so far by the remaining groups. The pseudo code for the CGSO algorithm is listed in Table

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