Continuous Domain Ant Colony Algorithm And Its Application In Power System Economic Dispatch

Ant colony optimization algorithm is a meta-heuristic optimization algorithm designed based on ants foraging behavior.It is applied to classic discrete combination optimization problems such as traveling salesman problem,quadratic distribution problem and job shop scheduling problem.In the field of optimization,the variables of many problems are often continuous.Therefore,expanding the discrete ant colony algorithm to the continuous domain is a new research hotspot.This paper mainly focuses on the continuous and systematic ant colony algorithm and has achieved the following results:Aiming at the problem that the single improvement method has limited effectiveness,a dynamic partitioned hybrid continuous domain ant colony optimization algorithm(DPHACO)is proposed.The algorithm divides the solution into two parts,a good solution and a bad solution,and dynamically adjusts the number of good solutions and bad solutions in the iterative process.For the optimal solution,the local search strategy is used for preprocessing,which can improve the convergence accuracy of the algorithm.For the poor solutions,the random search strategy is used for preprocessing,which can expand the search scope,increase the diversity of solutions,and enhance search capabilities.The proposed algorithm is tested by standard test functions.The results show that the improved strategy can effectively improve the quality of the solution.To solve the problem of low utilization rate of individual information in continuous domain ant colony optimization(ACOR),a continuous domain ant colony optimization algorithm(ICACO)based on information exchange strategy was proposed.The ICACO algorithm selects a part of the solution during the update of the solution,uses the information exchange strategy to process the candidate solution,and adopts a greedy method to accept the candidate solution that can improve the quality of the solution.The proposed algorithm is tested by the standard test function.The experimental results show that the ICACO algorithm can effectively speed up the convergence of ACOR algorithm and improve the accuracy of search results.This algorithm has better global search ability and better performance than related improved continuous domain ant colony algorithm and other intelligent optimization algorithms.For the power system economic scheduling problem,an improved continuous domain ant colony algorithm is applied to this problem.Power system economic dispatch is a non-convex optimization problem,including some practical features such as valve point effect,prohibition interval,slope limit,and transmission loss.Continuous domain ant colony optimization algorithm is a relatively new type of swarm intelligence optimization algorithm.It has the characteristics of simple and easy implementation,parallel search and high computational efficiency.It is suitable for complex optimization problems and can find global optimal solutions with a large probability.This paper mainly applies the improved continuous domain ant colony optimization algorithm to the power system economic dispatch calculation study.The data of the benchmark test system is simulated and compared with the standard continuous domain ant colony algorithm.It is proved that the improved ant colony algorithm is more effective.