NEURAL COMPUTING & APPLICATIONS, cilt.32, ss.2751-2783, 2020 (SCI İndekslerine Giren Dergi)
This study introduces a derivative of the well-known optimization algorithm, Big Bang-Big Crunch (BB-BC), named Nuclear Fission-Nuclear Fusion-based BB-BC, simply referred to as N2F. Broadly preferred in the engineering optimization community, BB-BC provides accurate solutions with reasonably fast convergence rates for many engineering problems. Regardless, the algorithm often suffers from stagnation issues. More specifically, for some problems, BB-BC either converges prematurely or exploits the promising regions inefficiently, both of which prevent obtaining the optimal solution. To overcome such problems, N2F algorithm is proposed, inspired by two major phenomena of nuclear physics: fission and fusion reactions. In N2F, two concepts named "Nuclear Fission" and "Nuclear Fusion" are introduced, replacing the "Big Bang" and "Big Crunch" phases of BB-BC, respectively. With the "Nuclear Fission" phase represented through a parameter named amplification factor, premature convergence issues are eliminated to a great extent. Meanwhile, convergence rate and exploitation capability of the algorithm are enhanced largely through a precision control parameter named magnification factor, in the "Nuclear Fusion" phase. The performance of N2F algorithm is investigated through unconstrained test functions and compared with the conventional BB-BC and other metaheuristics including genetic algorithm, Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC), Drone Squadron Optimization (DSO) and Salp Swarm Algorithm (SSA). Then, further analyses are performed with constrained design benchmarks, validating the applicability of N2F to engineering problems. With superior statistical performance compared to BB-BC, GA, PSO, ABC, DSO and SSA in unconstrained problems and improved results with respect to the literature studies, N2F is proven to be an efficient and robust optimization algorithm.