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Living Space Evolution: A New Crowd Based Computational Approach

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  • Xiao Laisheng

    (Educational Information Center, Guangdong Ocean University, Zhanjiang 524088, China)

Abstract

Inspired by the life cycle and survival of the fittest and combined with the consideration of living space information, a new computational intelligence approach, namely, living space evolution (LSE), is presented. LSE has reflected two new ideas. One is living space evolution: under the guidance of living space information, the offspring of life concentrate and evolve continuously towards richer living spaces. The other is multiple offspring reproduction: simulating real life in nature, a life can reproduce multiple offspring within one generation. In this work, LSE dynamic model, its flow, and pseudocodes are described in detail. A digital simulation has shown the procedure of LSE living space evolution. Furthermore, two applications of using LSE are employed to demonstrate its effectiveness and applicability. One is to apply it to the optimization for continuous functions, and the other is to use it as an optimization tool for routing protocol in wireless sensor network that is a discrete problem in real world. Research has shown that LSE is effective for the optimization for the continuous functions and also applicable for the discrete problem in real world. In addition, LSE has a special ability to balance search process from exploration to exploitation gradually.

Suggested Citation

  • Xiao Laisheng, 2015. "Living Space Evolution: A New Crowd Based Computational Approach," International Journal of Distributed Sensor Networks, , vol. 11(10), pages 804512-8045, October.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:10:p:804512
    DOI: 10.1155/2015/804512
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