A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem

Merz, Peter; Freisleben, Bernd

doi:10.1109/cec.1999.785529

Cited by 102 publications

(84 citation statements)

References 29 publications

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“…The benefit of MAs in EAs is that they use local search for the presented problem [54]. The pseudo-code of MA local search is shown in Figure 4.…”

Section: Memetic Techniquementioning

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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“…The benefit of MAs in EAs is that they use local search for the presented problem [54]. The pseudo-code of MA local search is shown in Figure 4.…”

Section: Memetic Techniquementioning

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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“…identifying higher quality solutions) than traditional EAs for some problem domains. As a result, MAs are gaining wide acceptance, in particular in well-known combinatorial optimization problems where large instances have been solved to optimality and where other meta-heuristics have failed to produce comparable results (see for example [3] for a comparison of MAs against other approaches for the Quadratic Assignment Problem). [4] Metaheuristics such as GA or PSO amazing in the last iterations and couldn't continue their path correctly.…”

Section: Memetic Algorithmmentioning

confidence: 99%

Memetic-CLA-PSO: A Hybrid Model for Optimization

Akhtari

Meybodi

2011

2011 UkSim 13th International Conference on Computer Modelling and Simulation

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“…The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. So far, MAs are extensively used for solving many optimization problems, such as scheduling problems [20], [1], travel salesman problems [27], combinatorial optimization problems [13], quadratic assignment problems [24] and other applications [6], [16].…”

Section: Behaviors Of Agentsmentioning

confidence: 99%

Agent Based Evolutionary Dynamic Optimization

Yang

Wang

et al. 2010

Agent-Based Evolutionary Search

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Summary Agent-based Evolutionary Search (AES) has attracted a growing interest from the evolutionary computation community in recent years due to its robust ability in solving large scale problems, ranging from online trading, disaster response, modeling social structures to financial investment planning. In order to solve these problems, a great variety of intelligent techniques have been developed to improve the framework and efficiency of AES. This chapter investigates an agent based evolutionary search algorithm in which the agents are updated and co-evolve to track dynamic optimum by imitating the exhibited feature of living organism. In the proposed algorithm, all agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the predefined energy function, individual agent can compete with its neighbors and also can acquire knowledge through cumulative information. For the purpose of maintaining the diversity of the population, random immigrants and adaptive primal dual mapping schemes are incorporated. Simulation experiments on a set of dynamic benchmark problems show the proposed AES algorithm can yield a better performance on dynamic optimization problems (DOPs) in comparison with several peer algorithms.

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A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem

Cited by 102 publications

References 29 publications

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

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

Memetic-CLA-PSO: A Hybrid Model for Optimization

Agent Based Evolutionary Dynamic Optimization

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