Multimapping Chaotic Mind Evaluation Algorithm

In order to overcome the disadvantages of Simple Mind Evaluation Algorithm (SMEA), such as the generation of the initial population is random and redundant, MEA and chaos are hybridized to form Multimapping Chaotic MEA (MCMEA), which reasonably combines the population-based evolutionary searching ability of MEA and chaotic searching behavior. In this method, two different chaos-mapping optimizations are introduced in different phases of population evolution. The chaotic ergodicity guides the evaluation to reach global optimum or its good approximation with high probability. The character of memory and optimum solution of the present generation are used to instruct the chaos search to improve searching efficiency. The test case confirmed the effectiveness, the flexibility and suitability of the proposed MCMEA.