Social cognitive optimization with tent map for combined heat and power economic dispatch

Combined heat and power economic dispatch (CHPED) problem is a sophisticated constrained nonlinear optimization problem in a heat and power production system for assigning heat and power production to minimize the production costs. To address this challenging problem, a novel social cognitive optimization algorithm with tent map (TSCO) is presented for solving the CHPED problem. To handle the equality constraints in heat and power balance constraints, adaptive constraints relaxing rule is adopted in constraint processing. The novelty of our work lies in the introduction of a new powerful TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of the presented algorithm is validated by conducting 2 typical CHPED cases. The numerical results show that the proposed approach has better convergence speed and solution quality than all other existing state-of-the-art algorithms.Comment: Accepted by International Transactions on Electrical Energy System

Handling equality constraints by adaptive relaxing rule for swarm algorithms

The adaptive constraints relaxing rule for swarm algorithms to handle with the problems with eqaulity constraints is presented. The feasible space of such problems may be similiar to ridge function class, which is hard for applying swarm algorithms. To enter the solution space more easily, the relaxed quasi feasible space is introduced and shrinked adaptively. The experimental results on benchmark functions are compared with the performance of other algorithms, which show its efficiency