文件列表:

CEC2005 (0, 2016-04-27)
CEC2005\A1.m (6794, 2016-04-27)
CEC2005\ACPSO.m (6994, 2016-04-27)
CEC2005\EF8F2_func_data.mat (984, 2016-04-27)
CEC2005\E_ScafferF6_M_D10.mat (984, 2016-04-27)
CEC2005\E_ScafferF6_M_D2.mat (216, 2016-04-27)
CEC2005\E_ScafferF6_M_D30.mat (7384, 2016-04-27)
CEC2005\E_ScafferF6_M_D50.mat (20184, 2016-04-27)
CEC2005\E_ScafferF6_func_data.mat (984, 2016-04-27)
CEC2005\ackley_M_D10.mat (984, 2016-04-27)
CEC2005\ackley_M_D2.mat (216, 2016-04-27)
CEC2005\ackley_M_D30.mat (7384, 2016-04-27)
CEC2005\ackley_M_D50.mat (20184, 2016-04-27)
CEC2005\ackley_func_data.mat (984, 2016-04-27)
CEC2005\automataActSel.m (347, 2016-04-27)
CEC2005\automataProbUp.m (636, 2016-04-27)
CEC2005\b.m (86, 2016-04-27)
CEC2005\benchmark_func.m (27327, 2016-04-27)
CEC2005\body.m (1681, 2016-04-27)
CEC2005\elliptic_M_D10.mat (984, 2016-04-27)
CEC2005\elliptic_M_D2.mat (216, 2016-04-27)
CEC2005\elliptic_M_D30.mat (7384, 2016-04-27)
CEC2005\elliptic_M_D50.mat (20184, 2016-04-27)
CEC2005\exemplar.m (1042, 2016-04-27)
CEC2005\fbias_data.mat (248, 2016-04-27)
CEC2005\func_plot.m (1716, 2016-04-27)
CEC2005\global_optima.mat (20184, 2016-04-27)
CEC2005\griewank_M_D10.mat (984, 2016-04-27)
CEC2005\griewank_M_D2.mat (216, 2016-04-27)
CEC2005\griewank_M_D30.mat (7384, 2016-04-27)
CEC2005\griewank_M_D50.mat (20184, 2016-04-27)
CEC2005\griewank_func_data.mat (984, 2016-04-27)
CEC2005\high_cond_elliptic_rot_data.mat (984, 2016-04-27)
CEC2005\hybrid_func1_M_D10.mat (8792, 2016-04-27)
CEC2005\hybrid_func1_M_D2.mat (7592, 2016-04-27)
CEC2005\hybrid_func1_M_D30.mat (72792, 2016-04-27)
... ...

# Adaptive-Cooperative-PSO

Matlab codes for Adaptive Cooperative Particle Swarm Optimizer (ACPSO) algorithm [1].

Full Text

Abstract

An Adaptive Cooperative Particle Swarm Optimizer (ACPSO) is introduced in this paper, which facilitates cooperation technique through usage of Learning Automata (LA) algorithm. Cooperative learning strategy of ACPSO optimizes the problem collaboratively and evaluates it in different contexts. In ACPSO algorithm, a set of learning automata associated with dimensions of the problem are trying to find the correlated variables of the search space and optimize the problem intelligently. This collective behavior of ACPSO will fulfill the task of adaptive selection of swarm members. Simulations were conducted on four types of benchmark suits which contain three state-of-the-arts numerical optimization benchmark functions in addition to one new set of active coordinate rotated test functions. The results demonstrate the learning ability of ACPSO in finding correlated variables of the search space and also describe how efficiently it can optimize the coordinate rotated multimodal problems, composition functions and high-dimensional multimodal problems.

Reference

[1] Mohammad Hasanzadeh, Mohammad Reza Meybodi, and Mohammad Mehdi Ebadzadeh" Adaptive cooperative particle swarm optimizer ," Applied Intelligence, 2013, vol. 39, no. 2, pp.397-420.

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