applied sciences  
A New \"Good and Bad Groups-Based Optimizer\" for Solving Various Optimization Problems [PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 3, [PERSON] 4,*(c) and [PERSON] 5, 2  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/article/pdf/10.3390/appl1104382](https://www.mdpi.com/journals/article/pdf/10.3390/appl1104382)]([https://www.mdpi.com/journals/article/pdf/10.3390/appl1104382](https://www.mdpi.com/journals/article/pdf/10.3390/appl1104382))  
Footnote 3: [[https://www.mdpi.com/journals/article/pdf/10.3390/appl1104382](https://www.mdpi.com/journals/article/pdf/10.3390/appl1104382)]([https://www.mdpi.com/journals/article/pdf/10.3390/appl1104382](https://www.mdpi.com/journals/article/pdf/10.3390/appl1104382))  
Footnote 4: [[https://www.mdpi.com/](https://www.mdpi.com/)]([https://www.mdpi.com/](https://www.mdpi.com/))  
An optimization problem has a definite optimal solution called global optimal. Optimization algorithms with random displacement in the problem search space provide a solution to a problem that is not necessarily global optimal, but close to it. For this reason, the solution obtained using optimization algorithms is called the quasi-optimal solution [2]. An optimization algorithm that presents a quasi-optimal solution closer to the global optimal solution is a more appropriate algorithm. This issue has led to the introduction of many optimization algorithms by researchers. Various ideas have been applied in the design of optimization algorithms. These ideas are based on various natural phenomena, the behavior of living things, plants, the laws of physics, the rules of the game, etc. Optimization algorithms can be divided into four general groups based on the main design idea. These groups are physic-based optimization algorithms, swarm-based optimization algorithms, game-based optimization algorithms, and evolutionary-based optimization algorithms. Physic-based optimization algorithms are designed based on simulations of processes and physical laws. Simulated annealing (SA) is a physics-based optimization algorithm modeled on the process of annealing metals [3]. In physics, annealing is the heat treatment during which changes the physical and sometimes chemical properties of a material occur. During this process, the metal is first heated, then kept at a certain temperature, and finally gradually cooled. The momentum search algorithm (MSA) is another physics-based optimization algorithm based on simulation of momentum laws and [PERSON]'s laws of motion [4]. In MSA, the momentum that enters a bullet causes the bullet to motion toward quasi-optimal points in the search space. The gravitational search algorithm (GSA) is inspired by the physical law of gravity between objects at different distances from each other. According to this law, particles (or objects) in this universe always exert a force called gravity on each other, which is directly proportional to the mass of two objects and inversely proportional to the square of the distance between them. In GSA, the simulation of this concept is used in designing an optimizer for optimization problems [5]. Swarm-based optimization algorithms are modeled on various natural phenomena, the behavior of animals, plants, and other living organisms. Particle swarm optimization (PSO) is one of the oldest and most famous swarm-based optimization algorithms which is designed based on the simulation of group motion of birds [6]. The seagull optimization algorithm (SOA) is another swarm-based optimization algorithm that is designed based on simulating the migration and aggressive behavior of a seagull in nature [7]. The teaching-learning-based optimization (TLBO) was designed based on simulating the educational relationship between students and teacher that leads to student learning and progress.