Impact of popular chaotic maps on population initialization of Sailfish Optimizer Algorithm – An experimental study

Initial population in a swarm intelligence-based optimization algorithm plays an important role in avoiding local optima, early convergence, and global space exploration. If initial values provide proper coverage of the search space, then it may quickly lead to the optimal solution, whereas limited coverage may stick an algorithm into local optima. Several techniques are presented by researchers to properly initialize the population instead of random numbers. Chaos theory is a well-known concept utilized in designing an improved variant of various existing nature-inspired optimization algorithms. However, its impact on the initial population of a metaheuristic was never thoroughly studied before. This study investigates the impact of ten chaotic maps on the initial population of a sailfish optimizer algorithm (SFO). SFO is a recent metaheuristic algorithm which mimics the hunting behavior of sailfish. Ten chaotic maps have been used to initialize sailfish and sardine populations. All techniques (with or without chaotic maps) are tested with twenty-three classical benchmark functions. The numerical results, ranking and statistical analysis show that chaotic maps are useful in avoiding local optima and are effective for improving the global search capabilities of a metaheuristic.