5/20/2023 0 Comments Vmoptimizer reviewGBO inspired by the gradient-based Newton’s method. GBO algorithm is an example of the modern metaheuristic population-based algorithms proposed by Ahmadianfar et al. The main features of these methods are their robustness in achieving the solutions and flexibility in adapting the problems. Also, they emulate the behavior of swarming social insects for seasonal migrations, looking for food or safety. ![]() Population-based algorithms are usually inspired by social insect colonies and animal societies. (4) Swarm-based like Cuckoo search algorithm (CSA), moth flame optimization (MFO), gradient-based optimizer (GBO) and others. (3) Physics-based algorithms, such as Central Force Optimization (CFO), Gravitational Search Algorithm (GSA), and Big Bang Big Crunch (BBBC). (2) Human-based algorithms, such as Tabu search (TS), Translation Lookaside Buffer (TLB), and Socio-evolution and Learning Optimization (SELO). Metaheuristic algorithms are divided into four classes: (1) Evolutionary Algorithms (EAs), such as Genetic Algorithm (GA), Genetic Programming (GP), and Differential Evolution. It can easily adapt to different kinds of optimization problems by using parameter tuning and modifying the operations. (ii) Metaheuristic algorithms which are also known as population-based methods. For instance, Simulated Annealing (SA) and Hill-Climbing (HC). Figure 1, the main category of the optimization algorithms: (i) Heuristic approach which also known as a single solution-based, where it contains special heuristic methods. In other words, the optimization algorithms ideas are nature-inspired. ![]() Optimization algorithms were introduced based on the behaviors of various organisms.
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