<th id="5nh9l"></th><strike id="5nh9l"></strike><th id="5nh9l"><noframes id="5nh9l"><th id="5nh9l"></th><strike id="5nh9l"></strike>
<progress id="5nh9l"><noframes id="5nh9l"><th id="5nh9l"><noframes id="5nh9l">
<th id="5nh9l"></th> <strike id="5nh9l"><noframes id="5nh9l"><span id="5nh9l"></span>
<progress id="5nh9l"><noframes id="5nh9l"><span id="5nh9l"><noframes id="5nh9l"><span id="5nh9l"></span><strike id="5nh9l"><noframes id="5nh9l"><strike id="5nh9l"></strike>
<span id="5nh9l"><noframes id="5nh9l">
<span id="5nh9l"><noframes id="5nh9l">
<span id="5nh9l"></span><span id="5nh9l"><video id="5nh9l"></video></span>
<th id="5nh9l"><noframes id="5nh9l"><th id="5nh9l"></th>
<progress id="5nh9l"><noframes id="5nh9l">

基于自適應搜索的免疫粒子群算法

Immune particle swarm optimization algorithm based on the adaptive search strategy

  • 摘要: 經典粒子群算法由于多樣性差而陷入局部最優,從而造成早熟停滯現象.為克服上述缺點,本文結合人工免疫算法,提出一種基于自適應搜索的免疫粒子群算法.首先,該算法改善了濃度機制;然后由粒子最大濃度值來控制子種群數目以充分利用粒子種群資源;最后對劣質子種群進行疫苗接種,利用粒子最大濃度值調節接種疫苗的搜索范圍,不僅避免了種群退化現象,而且提高了算法的收斂精度和全局搜索能力.仿真結果表明該算法求解復雜函數優化問題的有效性和優越性.

     

    Abstract: The particle swarm algorithm is often trapped in a local optimum due to poor diversity, resulting in a premature stagnation phenomenon. In order to overcome this shortcoming, an immune particle swarm optimization algorithm based on the adaptive search strategy was proposed in this paper. Firstly, the concentration mechanism was improved. Secondly, in order to make full use of the resources of the particle population, the number of particles of sub-populations was controlled by the maximum concentration of particles. Finally, the inferior sub-populations were vaccinated, and the maximum concentration of particles was used to control the search range of the vaccine, so the population degradation was avoided, and the convergence accuracy and the global search ability of the algorithm were improved. Simulation results show the effectiveness and superiority of the proposed algorithm in solving the complex function optimization problems.

     

/

返回文章
返回
<th id="5nh9l"></th><strike id="5nh9l"></strike><th id="5nh9l"><noframes id="5nh9l"><th id="5nh9l"></th><strike id="5nh9l"></strike>
<progress id="5nh9l"><noframes id="5nh9l"><th id="5nh9l"><noframes id="5nh9l">
<th id="5nh9l"></th> <strike id="5nh9l"><noframes id="5nh9l"><span id="5nh9l"></span>
<progress id="5nh9l"><noframes id="5nh9l"><span id="5nh9l"><noframes id="5nh9l"><span id="5nh9l"></span><strike id="5nh9l"><noframes id="5nh9l"><strike id="5nh9l"></strike>
<span id="5nh9l"><noframes id="5nh9l">
<span id="5nh9l"><noframes id="5nh9l">
<span id="5nh9l"></span><span id="5nh9l"><video id="5nh9l"></video></span>
<th id="5nh9l"><noframes id="5nh9l"><th id="5nh9l"></th>
<progress id="5nh9l"><noframes id="5nh9l">
259luxu-164