Abstract
Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.
Highlights
Optimization is the process in which the best solution to a particular problem is selected from a set of possible solutions
These results indicate the ability of the good and bad groups-based optimizer (GBGBO) to solve these types of objective functions and the superiority of the proposed algorithm over other algorithms
Optimization algorithms with random search space scanning are able to provide quasi-optimal solutions to optimization problems
Summary
Optimization is the process in which the best solution (based on a set of constraints) to a particular problem is selected from a set of possible solutions. When an optimization problem is expressed, it must be modeled mathematically. In this modeling, the objectives of the problem and the limitations must be considered. An optimization problem has three main parts: the problem variables, the primary objects of the problem including constraints, and the secondary objects of the problem including the objective functions of the problem [1]. After designing the optimization problem, the step is to solve the optimization problem using a suitable method. Optimization algorithms always have a special application in solving optimization problems. Optimization algorithms attempt to provide a solution by randomly scanning the search space
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