Summary of New Group Intelligent Optimization Algorithms
摘要
Intelligent optimization algorithms could be divided into four categories: nature-like optimization algorithm, evolutionary algorithm, plant growth simulation algorithm，and swarm intelligence optimization algorithm.The swarm intelligence optimization algorithm was the most important type of algorithm.It played an important role in solving complex engineering problems, and together with image processing, fault detection, path planning, particle filtering, feature selection, production scheduling, intrusion detection, support vector machines, wireless sensors, neural network models, and got more extensive applications in other fields.In recent years, intelligent optimization algorithms such as bat algorithm, fruit fly optimization algorithm, whale optimization algorithm, salp swarm algorithm, and harris hawks optimization algorithm were widely used.Based on these five new swarm intelligence optimization algorithm, the model, characteristics, improvement strategies and application fields of the algorithm were reviewed.It analyzed the development opportunities and future trends it faced from theoretical investigations, improvement strategy and application studies, and provided a guidance on algorithm application.Findings showed that swarm intelligence optimization algorithm could perform well on many classic problems, but still should be expanded in the fields of multi-objective optimization, multi-constraint optimization, dynamic optimization, and mixed variable optimization.Effective parameter control of different groups of intelligent optimization algorithm in the face of various specific problems was still the focus of future studies.Co-evolution from populations, exploring more efficient hybrid methods and search strategies could be feasible solutions.