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📋📋📋本文目录如下：🎁🎁🎁
目录
💥1 概述
📚2 运行结果
🎉3 参考文献
🌈4 Matlab代码及详细文章
💥1 概述
ev-MOGA多目标进化算法由瓦伦西亚理工大学的预测控制和启发式优化小组开发。ev-MOGA是一种基于ε优势概念的精英主义多目标进化算法。ev-MOGA试图在有限的内存资源下以智能分布式方式获得对帕累托阵线的良好近似。它还动态调整帕累托前线的极限。
文献来源：
M. Martínez, J.M. Herrero, J. Sanchis, X. Blasco and S. García-Nieto. Applied Pareto multi-objective optimization by stochastic solvers. Engineering Applications of Artificial Intelligence. Vol. 22 pp. 455 - 465, 2009 (ISSN:0952-1976).
📚2 运行结果
部分代码：
%% evMOGA example 4
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Minimal algorithm parameters set (problem characteristics)
clear eMOGA
eMOGA.objfun='mop3'; % m-function name for objectives computation
eMOGA.objfun_dim=2; % Objective space dimension
eMOGA.searchspaceUB=[pi pi]; % Search space upper bound
eMOGA.searchspaceLB=[-pi -pi]; % Search space lower bound
eMOGA.Nind_P= 250; % Individuals for the P population
eMOGA.Generations= 100; % Number of generations
eMOGA.n_div= 200; % Number of division for each dimension
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Algorithm execution
[pfront,pset,eMOGA]=evMOGA(eMOGA);
%% Now evMOGA is run again but using pfront and pset obtained in the previous run
eMOGA.subpobIni=pset;
eMOGA.subpobIni_obj=pfront;
figure % to show new result in another figure
[pfront2,pset2,eMOGA]=evMOGA(eMOGA);
🎉3 参考文献
部分理论来源于网络，如有侵权请联系删除。
[1] M. Martínez, J.M. Herrero, J. Sanchis, X. Blasco and S. García-Nieto. Applied Pareto multi-objective optimization by stochastic solvers. Engineering Applications of Artificial Intelligence. Vol. 22 pp. 455 - 465, 2009 (ISSN:0952-1976).
The algorithm is also described in:
[2] J.M. Herrero, M. Martínez, J. Sanchis and X. Blasco. Well-Distributed Pareto Front by Using the epsilon-MOGA Evolutionary Algorithm. Lecture Notes in Computer Science, 4507, pp. 292-299, 2007. Springer-Verlag. (ISSN: 0302-9743)
ev-MOGA has been used in:
[3] J.M. Herrero, X. Blasco, M. Martínez, C. Ramos and J. Sanchis. Robust Identification of a Greenhouse Model using Multi-objective Evolutionary Algorithms. Biosystems Engineering. Vol. 98, Num. 3, pp. 335 - 346, Nov 2007. (ISSN 1537-5110)
[4] J.M. Herrero, X. Blasco , M. Martínez, J. Sanchis. Multiobjective Tuning of Robust PID Controllers Using Evolutionary Algorithms. Lecture Notes in Computer Science, 4974, pp. 515 - 524, 2008. Springer-Verlag. (ISSN: 0302-9743)
[5] J. M. Herrero, S. García-Nieto, X. Blasco, V. Romero-García, J. V. Sánchez-Pérez and L. M. Garcia-Raffi. Optimization of sonic crystal attenuation properties by ev-MOGA multiobjective evolutionary algorithm. Structural and Multidisciplinary Optimization. Vol. 39, num. 2, pp. 203 - 215, 2009 (ISSN:1615-1488).
[6] G. Reynoso, X. Blasco, J. Sanchis. Diseño Multiobjetivo de controladores PID para el Benchmark de Control 2008-2009. Revista Iberoamericana de Automática e Informática Industrial. Vol. 6, Num. 4, pp. 93 - 103 , 2009. (ISSN: 1697-7912)
[7] E. Afzalan, M. Joorabian. Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using epsilon-multi-objective genetic algorithm variable.
Electrical Power and Energy Systems 52 (2013) 55–67
