Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation

Yen, Gary G.; Lu, Haibing

doi:10.1109/tevc.2003.810068

Cited by 174 publications

(70 citation statements)

References 18 publications

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“…For example, Jensen has employed advanced data structures to improve the run-time complexity of some popular MOEAs (e.g. NSGA-II) [53], while Yen et al have proposed an approach based on the usage of dynamic populations [104]. In another recent work [69], the idea of transforming a high-dimensional multiobjective problem into a biobjective optimization problem is exploited within an MOEA.…”

Section: Discussionmentioning

confidence: 99%

System-Level Modelling and Design Space Exploration for Multiprocessor Embedded System-on-Chip Architectures

Erbas¹

2006

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Section: Discussionmentioning

confidence: 99%

System-Level Modelling and Design Space Exploration for Multiprocessor Embedded System-on-Chip Architectures

Erbas¹

2006

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“…The process of systematically optimizing a set of objective functions at the same time is known as multi objective optimization (MOO) or vector optimization [29]. According to Lu [30] the optimal solutions obtained by individual optimization of the objectives are not a feasible solution to the multiobjective problem. Most practical optimization problems need the synchronized optimization of more than one objective function.…”

Section: Multi-objective Optimization (Moo)mentioning

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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“…In the last decade, evolutionary approaches have been the primary tools to solve real-world, multiobjective, optimization problems, such as Multiobjective Genetic Algorithm (MOGA) (Fonseca and Fleming 1993), Nondominated Sorting Genetic Algorithm (NSGA) (Srinivas and Deb 1994), Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999), improved SPEA (SPEA2) (Zitzler et al 2001), Pareto-Archived Evolution Strategy (PAES) (Knowles and Corne 2000), Fast Nondominated Sorting Genetic Algorithm (NSGA-II) (Deb et al 2002), Multi-objective Evolutionary Algorithm (MEA) (Sarker et al 2002), Dynamic Multi-objective Evolutionary Algorithm (DMOEA) (Yen and Lu 2003), and Differential Evolution for Multi-objective Optimization (DEMO) (Robič and Filipič 2005). All of these methods attempted to design effective and efficient algorithms to improve the abilities of the convergence and the diversity of the solution.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-objective Immune Algorithm for Optimal Design of Truss Structures

Xie

Tang

et al. 2016

Journal of Asian Architecture and Building Engineering

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In this paper, an adaptive immune clone selection algorithm for multi-objective optimization (AICSAMO) is proposed. A novel adaptive polynomial mutation operator with dynamic mutation probability is employed in AICSAMO. This adaptive mutation operator executes a rapid global search at the earlier stage of the algorithm and a fine-tuning search at the later stage of the algorithm, which adopts generation-dependent parameters to improve the convergence speed and global optimum searching ability. The effectiveness of AICSAMO is evaluated through the truss sizing and shape optimization problems of a 10-bar plane truss and a 25-bar space truss. According to the comparison of AICSAMO with various multi-objective optimization algorithms developed recently, the simulation results illustrate that AICSAMO has remarkable performance in finding a wider spread of optimal solutions and in maintaining better uniformity of the solutions with better convergence.

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Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation

Cited by 174 publications

References 18 publications

System-Level Modelling and Design Space Exploration for Multiprocessor Embedded System-on-Chip Architectures

System-Level Modelling and Design Space Exploration for Multiprocessor Embedded System-on-Chip Architectures

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

An Adaptive Multi-objective Immune Algorithm for Optimal Design of Truss Structures

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