An adaptive random walk algorithm with compulsive evolution algorithm for heat exchange network synthesis
When the RWCE(random walk algorithm with compulsive evolution)algorithm was applied to energy system optimization,the maximum step length affected both the range of the current feasible search domain and the evolution of integer variables.The fixed parameter setting further reduced the probability of optimal solutions.Therefore the RWCE algorithm that integrated adaptive step size and opposition-based learning strategy was proposed.A stochastic dynamic step size was established to automatically motivate the beneficial step size valued to evolve continuously under the traction of the guiding parameter.On the basis,the individual evolution path was changed by the adaptive opposition-based learning,so that the algorithm could automatically search for the better step size at different stages of optimization and explore as many structures as possible,so as to give full play to the global search and local exploitation capability of the algorithm.Finally,three typical medium-to-large scale cases H6C10,H10C10 and H13C7 were studied and evaluated.The results show that the proposed method can further improve the algorithm's search capability.
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