Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization

Tan, Kay Chen; Lee, T.H.; Khor, E. F.

doi:10.1109/4235.974840

Cited by 286 publications

(119 citation statements)

References 21 publications

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“…Niche techniques aim at preserving diversity in population, and the key problem is to determine the niche radius. Based on literature [15], an approximate calculation method for dynamic niche radius is given. As pointed out in [15], the tradeoffs for an -objective optimization problem is in the form of an ( −1)-dimensional hypersurface.…”

Section: Dominated Individuals Nondominated Individualsmentioning

confidence: 99%

“…Based on literature [15], an approximate calculation method for dynamic niche radius is given. As pointed out in [15], the tradeoffs for an -objective optimization problem is in the form of an ( −1)-dimensional hypersurface. For the two-objective optimization problem in this paper especially, its one-dimensional surface is actually a curve.…”

Section: Dominated Individuals Nondominated Individualsmentioning

confidence: 99%

See 1 more Smart Citation

QoS-Aware Multiobjective Optimization Algorithm for Web Services Selection with Deadline and Budget Constraints

Xian-zhong¹,

Yuan²,

Chen³

et al. 2014

Advances in Mechanical Engineering

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The problem of QoS-aware multiobjective optimization is an important issue for Web services selection in distributed computing environment. In this paper, a novel algorithm called MOASS (multiobjective optimization algorithm for web service selection) is proposed through analyzing the genetic operators such as constraint handling, the initial population generation, fitness assignment, and diversity preservation. Compared with MOEAWP (Yu et al., 2007), simulation results show that the feasible objective region can be filled uniformly with the optimal solutions obtained by MOASS under different test applications. In the case of higher constraints especially, MOASS can obtain more high-quality and evenly distributed nondominated solutions than MOEAWP.

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Section: Dominated Individuals Nondominated Individualsmentioning

confidence: 99%

Section: Dominated Individuals Nondominated Individualsmentioning

confidence: 99%

QoS-Aware Multiobjective Optimization Algorithm for Web Services Selection with Deadline and Budget Constraints

Xian-zhong¹,

Yuan²,

Chen³

et al. 2014

Advances in Mechanical Engineering

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“…The important goal of MOO algorithm is to find a set of solutions from which the best one is chosen. Based on Tan et al [13], the ability of evolutionary algorithms (EAs) to search for optimal solutions gives it the priority to be selected in MOO problems. EAs have the ability to explore different parts of the related algorithm in the optimal set because of the population-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

Saleh¹,

Shamsuddin²

2015

Int J Swarm Intel Evol Comput

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Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many realworld optimization problems include several contradictory objectives. Rather than single optimization, Multi-Objective Optimization (MOO) can be utilized as a set of optimal solutions to solve these problems. In this paper, Harmony Search (HS) and memetic approach was used to improve the performance of MOO with ESNN. Consequently, Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) was applied to improve ESNN structure and accuracy rates. Standard data sets from the UCI machine learning are used for evaluating the performance of this enhanced multi objective hybrid model. The experimental results have proved that the Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) gives better results in terms of accuracy and network structure. Unlike previous study mentioned earlier in Jin [14] and other single objective studies [18,19], this paper deals with an improved multi objective method to obtain simple and accurate ESNN. The proposed method evolves toward optimal values defined by several objectives with model accuracy and ESNN's structure to improve performance for classification problems. The remaining parts of this paper are organized as follows: Methods including: Evolving Spiking Neural Network, Multi Objective Differential Evolution are presented in section 2. In addition, section 3 clarifies the proposed method Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) used in this paper, Experimental design is discussed in section 4, section 5 explains in detail the results and discussion, and finally, section 6 concludes the paper with future works. MethodsThis section reviews the vital foundation of evolving spiking neural network (ESNN) and discusses the evolutionary algorithms that have been utilized for enhancement. In the first part, an introduction of ESNN, neuron coding, learning method and ESNN design, and its algorithm is presented. The second part focuses on the algorithms which are used for improvement of ESNN. The concepts and methods of multi objective optimization (MOO) are highlighted. After that, the literature focuses on the working of differential evolution, harmony search (HS) algorithm and memetic approach which is used to improve and enhance the performance of classification . Evolving spiking neural network (ESNN)Currently, several enhancements of SNN have been proposed. Wysoski improved one of these new models known as Evolving Spiking Neural Network (ESNN) [20]. Generally, ESNN used the principles of evolving connectionist systems (ECOS) where neurons are created incrementall...

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“…al. [8] also considered adaptive swarm size to adjust the swarmsize-based on an approximate tradeoff of the hyper-area already discovered by the swarm and taking experience from the dominant particles or leader particles. These efforts,though include adaptive swarm size, but the same algorithm has been used for both exploration and exploitation stage.…”

Section: Introductionmentioning

confidence: 99%

Static learning particle swarm optimization with enhanced exploration and exploitation using adaptive swarm size

Panda

Ghoshal

Konar

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract:In this paper, a novel Static Learning (SL) strategy to adaptively vary swarm size has been proposed and integrated with Particle Swarm Optimization algorithm. Besides, the whole population has been divided into two sub swarms, where particles of different sub swarms interact within their neighbourhood and the existence of better particle is determined by evaluating its survival probability. Proper resource based particle replacement scheme and a linear chaotic term has also been included to ensure preservation of diversity of the swarm. In addition, the PSO algorithm is divided into two phases, with relevant algorithmic modification for each phase. The first phase is assigned to focus solely on better exploration of the search space. The second phase focuses on better utilization of the explored information. The proposed Static Learning Particle Swarm Optimization with Enhanced Exploration and Exploitation using Adaptive Swarm Size (SLPSO) algorithm is tested on a set of shifted and rotated benchmark problems and compared with six other recent state-of-the-art PSO algorithms. The proposed (SLPSO) algorithm demonstrates superior performance over other PSO variants.

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Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization

Cited by 286 publications

References 21 publications

QoS-Aware Multiobjective Optimization Algorithm for Web Services Selection with Deadline and Budget Constraints

QoS-Aware Multiobjective Optimization Algorithm for Web Services Selection with Deadline and Budget Constraints

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

Static learning particle swarm optimization with enhanced exploration and exploitation using adaptive swarm size

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