| Citation: | LI J，ZHANG R C，PAN C Y，et al. Micro immune optimization algorithm for single objective probabilistic constrained programming[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（3）：525-537 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0288
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An immune optimization algorithm with a small population is proposed to solve a single objective probability constrained programming with no prior random distribution information. In the design of the algorithm, we develop an evolutionary framework with a micro population inspired by danger theory. Based on the amplitude of error of the estimated value, two approaches are proposed to estimate the individual’s objective values and each chance constraint’s probability respectively. According to the superior and inferior relationships among individuals, the population was divided into three types of sub-population for co-evolution. A version of individual life cycle is constructed, while adaptive crossover probability, adaptive mutation probability and adaptive mutation strategy as well as crossover strategy are designed to promote effective information exchange among the above sub-populations to co-evolve individuals in different directions. The results of numerical experiments show that the proposed algorithm has good search efficiency, search effect and noise reduction ability, and has certain competitiveness and application potential.
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