Description: Abstract:This paperproposed a new pareto-based multi-object evolutionary mi mune algorithm(PMEIA). PMEIA selected
optmi alnon-dominated antibodieswhichwere then reserved inmemory cellarchive, and introducedParzenwindow to calculate
entropy ofmemory cells. Updated thememory cell archive according to entropy ofmemory cells. This guarantees the conver-
gence to the true Pareto fron.t Moreover, the performance of clone selection was dependent on distribution in the objective
space, whichwas favorable forgetting awidely spreadPareto frontand mi proving convergence speed. Comparedwith the exis-
ted algorithms, the obtained solutions ofPMEIA havemuch betterperformance in the convergence, diversity and distribution.
To Search:
- [CHC] - This code is used in paper I write code,
- [EAFSA] - Minimum objective optimization for multi
- [080502] - Ant colony algorithm based on MATLAB Sim
- [DGPSO] - Solving constrained optimization problem
- [cccc] - Abstract:In order to effectively solve m
- [dddd] - Dynam icMultiobjectiveOptim ization Immu
- [NSGA2] - Multi-objective evolutionary algorithm,
- [diaodu] - Multi-objective optimization, optimal sc
- [pso-multiobjective] - multi-objective ,PSO
- [NSGA] - matlab write PSO-based multi-objective o
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基于Pareto的多目标进化免疫算法.caj
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