Analysis and Research of Several New Intelligent Optimization Algorithms

Abstract

Intelligent optimization algorithms (IOA) refer to a kind of algorithm that is used to solve optimization problems by imitating the survival and evolution process of natural creatures or physical phenomena as the algorithm principle. The well-known intelligent optimization algorithms include genetic algorithm, particle swarm optimization, simulated annealing algorithm, etc. Intelligent optimization algorithm is a heuristic method, which is widely used in solving optimization problems and provides some new ideas for solving some practical problems. With the advan-cement of science and technology and the increase in the complexity of application scenarios, traditional intelligent optimization algorithms can no longer satisfy optimization problems in terms of solving effects and accuracy. Therefore, new and more efficient intelligent optimization algorithms are constantly being proposed. Several new intelligent optimization algorithms have been proposed at home and abroad in recent years, such as butterfly optimization algorithm (BOA), moth-flame optimization (MFO), sine cosine algorithm (SCA), grasshopper optimization algorithm (GOA), Harris hawks optimization (HHO) and sparrow search algorithm (SSA). This paper describes the basic principle, algorithm steps, corresponding improvement strategies with advantages and disadvantages of each algorithm. To objectively compare the performance of each algorithm, this paper further evaluates the performance of each algorithm through 21 test functions of 3 types and 6 indicators. Finally, this paper summarizes the characteristics of the algorithm and prospects the development direction of intelligent optimization algorithm.

Keywords

• |intelligent optimization algorithms (ioa)|butterfly optimization algorithm (boa)|moth-flame optimi-zation (mfo)|sine cosine algorithm (sca)|grasshopper optimization algorithm (goa)|harris hawks optimization (hho)|sparrow search algorithm (ssa)