Abstract
Numerous optimization problems have been defined in different disciplines of science that must be optimized using effective techniques. Optimization algorithms are an effective and widely used method of solving optimization problems that are able to provide suitable solutions for optimization problems. In this paper, a new nature-based optimization algorithm called Snow Leopard Optimization Algorithm (SLOA) is designed that mimics the natural behaviors of snow leopards. SLOA is simulated in four phases including travel routes, hunting, reproduction, and mortality. The different phases of the proposed algorithm are described and then the mathematical modeling of the SLOA is presented in order to implement it on different optimization problems. A standard set of objective functions, including twenty-three functions, is used to evaluate the ability of the proposed algorithm to optimize and provide appropriate solutions for optimization problems. Also, the optimization results obtained from the proposed SLOA are compared with eight other well-known optimization algorithms. The optimization results show that the proposed SLOA has a high ability to solve various optimization problems. Also, the analysis and comparison of the optimization results obtained from the SLOA with the other eight algorithms shows that the SLOA is able to provide more appropriate quasi-optimal solutions and closer to the global optimal, and with better performance, it is much more competitive than similar algorithms.
Highlights
IntroductionThe goal of optimization is to find the best acceptable answer, given the limitations and needs of the problem [1]
The optimization results obtained using the Snow Leopard Optimization Algorithm (SLOA) are compared with the performance of eight optimization algorithms including Genetic Algorithm (GA) [23], Particle Swarm
Population-based optimization algorithms are among the stochastic solving methods of optimization problems that can provide acceptable quasi-optimal solutions to optimization problems
Summary
The goal of optimization is to find the best acceptable answer, given the limitations and needs of the problem [1]. There may be different solutions, and to compare them and select the optimal solution, a function called the objective function is defined. The choice of this function depends on the nature of the problem. Choosing the suitable objective function is one of the most important optimization steps [2]. An optimization problem can be defined from a mathematical point of view using the three main parts of variables, objective functions, and constraints [3]. Once the optimization problem is mathematically modelled, it must be optimized using the appropriate method
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