Artificial ecosystem optimization by means of fitness distance balance model for engineering design optimization

Abstract Optimization techniques have contributed to significant strides in complex real-world engineering problems. However, they must overcome several difficulties, such as the balance between capacities for exploitation and exploration avoiding local optimum. An enhanced Artificial Ecosystem (AEO) is proposed incorporating Fitness Distance Balance Model (FDB) handling various design optimization In optimizer, combined FDB aids selecting individuals who successfully contribute population-level searches. Therefore, model integrated with AEO algorithm increase solution quality nonlinear multidimensional situations. The FDBAEO developed six well-studied tasks considering welded beam, rolling element bearing, pressure vessel, speed reducer, planetary gear train, hydrostatic thrust bearing simulation outcomes were evaluated compared systemic other recent meta-heuristic approaches. findings demonstrated that reached global optimal point more successfully. It has promising abilities. Also, shows greater outperformance algorithms of Atomic Orbital Search, Arithmetic-Trigonometric, Beluga whale, Chef-Based, Optimizers. Moreover, it declares great superiority reported optimizers.