Meta-heuristic algorithms have successfully solved many real-world problems in recent years. Inspired by different natural phenomena, the algorithms with special search mechanisms can be good at tackling certain probl...
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Meta-heuristic algorithms have successfully solved many real-world problems in recent years. Inspired by different natural phenomena, the algorithms with special search mechanisms can be good at tackling certain problems. However, they may fail to solve other problems. Among the various approaches, hybridizing meta-heuristic algorithms may possibly help to enrich their search behaviors while promoting the search adaptability. Accordingly, an efficient hybrid population-based optimization framework, namely the HYPO, is proposed in this study in which two metaheuristic algorithms with different search ideas are connected by a dynamic contribution-based state transition scheme. Specifically, the dynamic transition scheme determines the directions of information transitions after considering the current contribution and system state at each iteration so that useful information can be shared and learnt between the concerned meta-heuristic algorithms throughout the search process. To carefully examine the effectiveness of the dynamic transition scheme, the proposed HYPO framework is compared against various well-known meta-heuristic algorithms on a set of large-scale benchmark functions and portfolio management problems of different scales in which the HYPO attains outstanding performances on the problems with complex features. Last but not least, the hybrid framework sheds lights on many possible directions for further improvements and investigations.
A new hybridized algorithm is developed to solve the combined heat and power economic dispatch(CHPED) *** to the non-convexity of the CHPED problem, the global optimum is difficult to achieve. The presented approach c...
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ISBN:
(纸本)9781538629185
A new hybridized algorithm is developed to solve the combined heat and power economic dispatch(CHPED) *** to the non-convexity of the CHPED problem, the global optimum is difficult to achieve. The presented approach combines the algorithms of harmony search(HS) and Nelder-Mead(NM), and the hybridized algorithm is called the NM-HS algorithm. The hybridized NM-HS algorithm improves the efficiency of the two original algorithms, and also the robustness. Two benchmark examples demonstrate the good performance of the NM-HS algorithm. The solutions obtained is better than, or at least is the same as, other methods.
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