A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

Optimization is the science that presents a solution among available solutions considering an optimization problem’s limitations. algorithms have been introduced as efficient tools for solving problems. These are designed based on various natural phenomena, behavior, lifestyle of living beings, physical laws, rules games, etc. In this paper, new algorithm called good and bad groups-based optimizer (GBGBO) to solve GBGBO, population members update under influence two groups named group group. The consists certain number with better fitness function than other worse population. GBGBO mathematically modeled its performance in problems was tested set twenty-three different objective functions. addition, further analysis, results obtained from proposed were compared eight algorithms: genetic (GA), particle swarm (PSO), gravitational search (GSA), teaching–learning-based (TLBO), gray wolf (GWO), whale (WOA), tunicate (TSA), marine predators (MPA). show has ability more competitive similar algorithms.