A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems
This work introduces a novel meta-heuristic for optimization problems, but it appears incremental as it applies a new animal-inspired method to existing benchmarks.
The authors proposed a new swarm intelligence algorithm called the artificial orca algorithm (AOA) that simulates multiple behaviors of orcas to solve problems, and it demonstrated superiority over state-of-the-art algorithms like ACO, BA, BSO, EHO, PSO, and WOA in experiments on maze games with four complexity levels, as measured by success rate, run time, and solution path size.
In this paper, a new swarm intelligence algorithm based on orca behaviors is proposed for problem solving. The algorithm called artificial orca algorithm (AOA) consists of simulating the orca lifestyle and in particular the social organization, the echolocation mechanism, and some hunting techniques. The originality of the proposal is that for the first time a meta-heuristic simulates simultaneously several behaviors of just one animal species. AOA was adapted to discrete problems and applied on the maze game with four level of complexity. A bunch of substantial experiments were undertaken to set the algorithm parameters for this issue. The algorithm performance was assessed by considering the success rate, the run time, and the solution path size. Finally, for comparison purposes, the authors conducted a set of experiments on state-of-the-art evolutionary algorithms, namely ACO, BA, BSO, EHO, PSO, and WOA. The overall obtained results clearly show the superiority of AOA over the other tested algorithms.