Accelerating sailfish optimization applied to unconstrained optimization problems on graphical processing unit

Abstract

The Sailfish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternates their attacks on group of prey. The SFO algorithm takes advantage of using a simple method for providing the dynamic balance between exploration and exploitation phases, creating the swarm diversity, avoiding local optima, and guaranteeing high convergence speed. However, taking a lot of time to solve optimization problems has become a challenge for metaheuristic algorithms. Due to independence of the metaheuristics components, parallel processing is a good option to reduce the computational time and to find high quality solutions that are close to the optimum with an acceptable cost. Nowadays, combination of parallel processing and metaheuristic algorithms can provide high performance solutions to quickly solve combinatorial optimization problems. In this paper, we elaborate a novel GPU based and accelerated method of sailfish optimizer (ASFO), which improves the execution time and speedup while maintaining the results of optimization in high quality. In depth of study, we present the implementation details and performance observations of ASFO algorithm. Also, a comparative study of accelerated and sequential SFO is performed on a set of standard benchmark optimization functions and it compared with other parallel algorithms to show the speed of proposed algorithm for solving unconstrained optimization problems. The results indicate the ability of proposed approach in continuous, non-separable, non-convex and scalable optimization problems.

Keywords

• Sailfish Optimizer (SFO)
• Accelerated Sailfish Optimizer (ASFO) unconstrained optimization problems
• Parallel processing
• Shared memory
• Graphic processing units
• CUDA