Snow Leopard Optimization Algorithm: A New Nature-Based Optimization Algorithm for Solving Optimization Problems

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.

A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process

“…The ability of an ant colony to find the shortest path between the colony and food sources has been the main idea in the design of the ACO. Hunting and attacking prey strategy, as well as the process of finding food sources among living organisms, has been a source of inspiration in designing various metaheuristic algorithms such as the Tunicate Search Algorithm (TSA) 11 , Reptile Search Algorithm (RSA) 12 , Whale Optimization Algorithm (WOA) 13 , Orca Predation Algorithm (OPA) 14 , Marine Predator Algorithm (MPA) 15 , Pelican Optimization Algorithm (POA) 16 , Snow Leopard Optimization Algorithm (SLOA) 17 , Gray Wolf Optimization (GWO) algorithm 18 , Artificial Gorilla Troops Optimizer (GTO) 19 , African Vultures Optimization Algorithm (AVOA) 20 , Farmland Fertility 21 , Spotted Hyena Optimizer (SHO) 22 , and Tree Seed Algorithm (TSA) 23 .…”

Extended Stochastic Coati Optimizer

“…Animal behaviors are commonly used as inspiration for the development of metaheuristics. Example of metaheuristics that mimic animal behavior is white shark optimizer (WSO) [11], reptile search algorithm (RSA) [12], marine predator algorithm (MPA) [13], cheetah optimizer (CO) [14], grey wolf optimizer (GWO) [15], clouded leopard optimizer (CLO) [16], snow leopard optimization algorithm (SLOA) [17], Komodo mlipir algorithm (KMA) [18], northern goshawk optimizer (NGO) [19], butterfly optimization algorithm (BOA) [20], red fox optimization algorithm (RFO) [21], remora optimization algorithm (ROA) [9], and so on. Some metaheuristics imitates the mechanism of traditional game, such as puzzle optimization algorithm (POA) [22], ring toss game-based optimization (RTGBO) [23], and so on.…”

Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization

“…Mathematically, any optimization problem can be represented using three key components: decision variables, constraints, and objective functions 2 . Problem-solving methods for addressing optimization problems can be categorized into two main groups: deterministic and stochastic techniques 3 . Deterministic methods effectively solve simple, linear, convex, continuous, differentiable, and low-dimensional optimization problems.…”