MULTI-STRATEGY COEVOLVING AGING PARTICLE OPTIMIZATION

被引:61
|
作者
Iacca, Giovanni [1 ]
Caraffini, Fabio [2 ]
Neri, Ferrante [2 ]
机构
[1] INCAS 3 Innovat Ctr Adv Sensors & Sensor Syst, NL-9400 AT Assen, Netherlands
[2] De Montfort Univ, Sch Comp Sci & Informat, CCI, Leicester LE1 9BH, Leics, England
基金
芬兰科学院;
关键词
Swarm intelligence; evolutionary computation; optimization algorithms; memetic computing; robotic arms; feedforward neural network; DIFFERENTIAL EVOLUTION ALGORITHM; NEURAL-NETWORK; MEMETIC ALGORITHMS; SWARM OPTIMIZATION; LOCAL SEARCH; PARAMETERS; ADAPTATION; ENSEMBLE; MUTATION; MODELS;
D O I
10.1142/S0129065714500087
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.
引用
收藏
页数:19
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