Multi-objective Biological Survival Optimizer with Application in Engineering Problems

Biological survival optimizer is a recent proposed swarm-based optimization algorithm, which is inspired by the nature behavior of prey and has two significant components, escape phase and adjustment phase. This paper proposes multi-objective Biological survival optimizer (MOBSO) based on non-dominated framework, in which an external set is utilized to store the obtained non-dominated solutions for guiding search. Besides that, elite selection mechanism is also employed to choose promising solutions from parent and offspring agents based on the non-dominated levels. The performance of MOBSO is also evaluated on a suite of benchmark problems with various features and three classical engineering design problems. Simulation comparison results considering different indicators show that MOBSO can generate competitive results compared with other state-of-art optimization techniques.