Optimal foraging algorithm with direction prediction

Abstract

Abstract Optimal foraging algorithm (OFA) was presented as a stochastic search algorithm to solve global optimization problems in 2017. As an emerging algorithm, there are many excellent potentials to be explored. To enhance the performance of OFA, a novel optimal foraging algorithm with direction prediction is presented in this paper, named OFA/DP. During the iterations, the population information can be fully utilized by OFA/DP. Once a new optimal solution is found, the evolutionary direction prediction strategy is applied to generate more potential candidates. In addition, considering the situation that the population does not evolve for a long time, which means that the algorithm has achieved the global optima or trapped into local optima. In this case, the Gaussian oscillation strategy is adopted to attempt to find a better solution, and escaping from local optima. To validate the efficiency of the proposed algorithm, the numerical experiments on 20 benchmark functions, 30 CEC test functions, 6 large scale functions, 28 CEC2017 constrained problems, 3 engineering problems, 6 unconstrained multi-objective functions and 10 constrained multi-objective functions are executed. The simulation results and the statistical test demonstrate that OFA/DP has a superior performance in most of functions with faster convergence speed.