Improved Coyote Optimization And Particle Swarm Algorithms And Their Application To Power Economic Dispatch

In power system operation,economic dispatch is one of the important optimization problems.The main objective of this problem is to find the most economical load distribution among generators under the condition of satisfying the equality and inequality constraints of multiple units and systems.It is not only important but also has practical significance and broad application prospect.Due to the characteristics of economic dispatch problems with different results,traditional methods are prone to fall into local optimum and high computational complexity,which can not meet the needs of these complex practical problems.With the continuous development and application of intelligent optimization algorithm,many researchers begin to focus on intelligent optimization algorithm to solve economic dispatch problem.In this paper,Particle Swarm Optimization（PSO）from the classic intelligent optimization algorithm and Coyote Optimization Algorithm（COA）from the latest intelligent optimization algorithm for research in order to obtain the intelligent optimization algorithm with good robustness,universality and operability can better solve the practical optimization problems such as economic dispatch.The main research contents of this paper are as follows:（1）COA is a population-based meta-heuristic optimization algorithm inspired by the canine species that live mainly in North America.This algorithm has the advantage of solving complex problems well because of its unique search framework,but it has the disadvantage of low search efficiency.To solve the existing problems of COA,a COA based on Information sharing and Static selection（ISCOA）was proposed.There are two major improvements in this algorithm.One is to propose a new learning strategy with an information sharing model,and the other is to change the dynamic greedy selection in the original COA to a static greedy algorithm.The experimental results show that ISCOA has better optimization performance compared with COA and other algorithms.（2）PSO imitates the behaviors of individuals in the social system,such as fish and birds,etc.It has the advantages of simple structure and strong exploitation ability,but it has the disadvantage of insufficient exploration ability.Dynamic Multi-swarm Differential Learning PSO（DMSDL-PSO）has solved the problems of PSO well,but there are still some shortcomings.To solve the problems of DMSDL-PSO,such as poor operability,complex structure,high computational complexity and low search efficiency,a Random Learning and quasi-Newton search PSO（RLPSOq）was proposed.There are two major improvements in this algorithm.One is the random learning strategy,the other is the introduction of improved quasi-Newton method.Experimental results of complex function optimization show that RLPSOq performs better than DMSDL-PSO and other PSO variants of learning mechanisms.（3）When the classical intelligent optimization algorithm is used to solve the economic dispatch problem,it has some problems,such as insufficient search ability and easy to fall into local optimization.In this paper,ISCOA and RLPSOq are applied to economic dispatch problem.First,ISCOA and RLPSOq are applied to simple economic dispatch problems respectively.They are then applied to complex economic dispatch problems.The simulation results of two types of economic dispatch problems show that both ISCOA and RLPSOq can provide better simulation results compared with other intelligent optimization algorithm algorithms.