A Distributed Sailfish Optimizer Based on Multi-Agent Systems for Solving Non-Convex and Scalable Optimization Problems Implemented on GPU

The SailFish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternate their attacks on a group of prey. The SFO algorithm takes advantage of using a simple method for providing a dynamic balance between the exploration and exploitation phases, creating the swarm diversity, avoiding local optima, and guaranteeing a high convergence speed. Nowadays, multi-agent systems and metaheuristic algorithms can provide high performance solutions for solving combinatorial optimization problems. These methods provide a prominent approach to reduce the execution time and improve the solution quality. In this paper, we elaborate a multi-agent based and distributed method for sailfish optimizer (DSFO), which improves the execution time and speeds up the algorithm, while maintaining the optimization results in a high quality. The Graphics Processing Units (GPUs) using Compute Unified Device Architecture (CUDA) are used for the massive computation requirements in this approach. In depth of the study, we present the implementation details and performance observations of the DSFO algorithm. Also a comparative study of the distributed and sequential SFO is performed on a set of standard benchmark optimization functions. Moreover, the execution time of the distributed SFO is compared with other parallel algorithms to show the speed of the proposed algorithm to solve the unconstrained optimization problems. The final results indicate that the proposed method is executed about maximum 14 times faster than the other parallel algorithms and shows the ability of DSFO for solving the non-separable, non-convex, and scalable optimization problems.