Enhanced self-adaptive evolutionary algorithm for numerical optimization

Feedforward neural network (FNN) is one of the most widely used and fastest-developed artificial neural networks. Much evolutionary computation (EC) methods have been used to optimize the weights of FNN. However, as the dimension of datasets increases, the number of weights also increases dramatically. On high-dimensional datasets, if EC methods are used directly to optimize the weights of FNN, it is impossible to obtain the optimal weights of the FNN by EC methods in an acceptable time. Feature selection is a method that can effectively reduce the computational complexity of FNN by reducing irrelevant and redundant features. It may be practical to optimize the FNN by EC methods if we first employ the feature selection for the large-scale datasets. In this paper, we present a self-adaptive parameter and strategy-based particle swarm optimization (SPS-PSO) algorithm to optimize FNN with feature selection. First, we propose an optimization model for FNN by transforming the designing of FNN into a weights optimization problem. Simultaneously, we present a feature selection optimization model. Second, we present an SPS-PSO algorithm. In this algorithm, we use the parameter and strategy self-adaptive mechanism. In addition, five candidate solution generating strategies (CSGS) are used. The experiments are divided into two groups. In the first group, SPS-PSO and three other EC methods are used to directly optimize the weights of FNN on eight datasets without any modification. In the second group, we first employ SPS-PSO-based feature selection on the original datasets and obtain eight relatively smaller datasets with the k-nearest neighbor (KNN) which is used as the evaluation function for saving time. Then, we use the new datasets as the inputs for FNN. We optimize the weights of FNN again by SPS-PSO and three other EC methods to investigate whether we can get similar or even better classification accuracy by comparing the results with that of the first group. The experimental results show that SPS-PSO has the advantage in optimizing the weights of FNN compared with the other EC methods. Meanwhile, the SPS-PSO-based feature selection can reduce the solution size and computational complexity while ensuring the classification accuracy when it is used to preprocess the datasets for FNN. In this method, a solution with an originally higher than 700 000 dimensions can be even reduced to hundreds of dimensions. INDEX TERMS Classification, evolutionary computation, feature selection, feedforward neural networks, self-adaptive, parameter adaptation, particle swarm optimization. I. INTRODUCTION Artificial neural network (ANN) [1], [2] is a hot research topic in the field of artificial intelligence [3] since the 1980s The associate editor coordinating the review of this manuscript and approving it for publication was Yongming Li. and it has been widely used as an effective classification method. ANN is a nonlinear and adaptive information processing system composed of a large number of interconnected processing units. It abstra...

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“…The strategy self-adaptive mechanism [38] and parameter self-adaptive mechanism of SPS-PSO are described as follows:…”

Section: B Self-adaptive Mechanism Of Sps-psomentioning

confidence: 99%

Large-Scale Feedforward Neural Network Optimization by a Self-Adaptive Strategy and Parameter Based Particle Swarm Optimization

2019

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“…Moreover, a self-adaptive learning based immune algorithm (SALIA) is proposed in [4] to improve the performance of immune based algorithms (IBAs) in tackling with diverse problems especially complex multi-modal and illconditioned problems. Self-adaptive search method is effective and efficient in improving the performance of the evolutionary algorithms which has been demonstrated by many relevant research works [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Based Cooperative Coevolutionary Algorithm for Large-scale Numerical Optimization

Zhang¹,

Xue²,

Zhao³

et al. 2015

IJCA

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“…Moreover, at different stages of evolution, different vector generation strategies coupled with specific control parameter values may be more effective than others. Recently, some selfadaptive algorithms are proposed to overcome the time consuming problem of the trial-and-error scheme in EAs [21,22]. Meanwhile, in the research field of DE, several self-adaptive based algorithms are proposed to improve the performance of DE [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem

Xue

Zhuang

Tian-quan

et al. 2014

J. of Syst. Eng. Electron.

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Cooperative jamming weapon-target assignment (CJWTA) problem is a key issue in electronic countermeasures (ECM). Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Finally, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution (SLDDE) algorithm is proposed by introducing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computational simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outperforms two algorithms which are proposed recently for the weapontarget assignment problems.

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Enhanced self-adaptive evolutionary algorithm for numerical optimization

Cited by 7 publications

References 19 publications

Large-Scale Feedforward Neural Network Optimization by a Self-Adaptive Strategy and Parameter Based Particle Swarm Optimization

Large-Scale Feedforward Neural Network Optimization by a Self-Adaptive Strategy and Parameter Based Particle Swarm Optimization

Self-adaptive Based Cooperative Coevolutionary Algorithm for Large-scale Numerical Optimization

Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem

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