Immune algorithm combined with estimation of distribution for traveling salesman problem

Xu, Zhe; Wang, Yirui; Li, Sheng; Liu, Yanting; Todo, Yuki; Gao, Shangce

doi:10.1002/tee.22247

Cited by 28 publications

(15 citation statements)

References 70 publications

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“…This paper provides a hybrid solution based on a Parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm and the Particle Swarm Optimization (PSO) algorithm for both locating and sizing problems, respectively. The Parallel Population-Based Incremental Learning (PPBIL) is based on the traditional PBIL algorithm [28], which belongs to the family of Estimation of Distribution Algorithms (EDAs) [29,30]. The PBIL algorithm uses probabilities to find the set of elements providing the best impact on the problem, modifying the learning rate to control the exploration of the solution space and the processing time of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

2018

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The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.Energies 2018, 11, 1018 2 of 27 generation [11,12]: reduction of power losses (due to transmission), improvement of voltage profiles and stability index, power factor enhancement, reduction of the harmonic distortion and increased line loadability, among others [13]. However, incorrect location or sizing procedures may result in voltage profiles out of conventional ranges, voltage fluctuations, line capacity violation, increased failure levels due to intermittent generation and higher costs associated to the DGs [14].Different methods have been developed to optimize the location and sizing of DGs. Those methods are aimed at reducing the computation time required and to improve the technical criteria of the grid, such as power losses, voltage profiles and power factor, among others [15,16]. For example, the work reported in [17] presents a hybrid method between the genetic algorithm proposed by Chu and Beasley (GACB) [18] and a heuristic approach to locate, select and size the feeders in a distributed generation environment. Such a strategy enables the reduction of the costs associated with the feeders (DGs) and the power losses, which is achieved by reducing the peak load using active power injection based on diesel-based DGs only. On the other hand, the work reported in [19] describes a multi-target approach based on the Particle Swarm Optimization (PSO) technique for locating the DGs, and an optimal flow analysis for sizing the DGs. That work considers several generators and different load models, thus enabling high penetration levels of distributed generation.A similar approach was presented in [20], which is a hybrid solution based on both the GACB and PSO. The main drawbacks of such a solution were the high level of power injection requested to the DGs and the lack of analysis of the computation time required by the proposed method. Another solution, based on the Loss ...

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Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

2018

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“…2) (10End if (11) End for (12) Else (13) Divide the swarm into two parts: producers and scroungers. (14) For = 1 to (15) If ( ==producer) (16) Birds flight using Eq. (5) //producer (17) Else (18) Birds flight using Eq.…”

Section: (1) Initialize the Parameter Values Of N M Fq P;mentioning

confidence: 99%

“…In recent years, many metaheuristic algorithms have been proposed to solve the TSP, such as ant colony algorithm (ACO) [1,2], artificial bee colony algorithm (ABC) [3], genetic algorithm (GA) [4], particle swarm optimization (PSO) [5], cuckoo search algorithm (CS) [6,7], bat algorithm (BA) [8,9], firefly algorithm (FA) [10], invasive weed optimization [11], bacterial evolutionary algorithm [12], dynamic multiscale region search algorithm (DMRSA) [13], a dual local search algorithm [14], immune algorithm [15], simulated annealing algorithm [16], and some hybrid algorithms [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Discrete Bird Swarm Algorithm Based on Information Entropy Matrix for Traveling Salesman Problem

Lin

Zhong

Lin

et al. 2018

Mathematical Problems in Engineering

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Although bird swarm optimization (BSA) algorithm shows excellent performance in solving continuous optimization problems, it is not an easy task to apply it solving the combination optimization problem such as traveling salesman problem (TSP). Therefore, this paper proposed a novel discrete BSA based on information entropy matrix (DBSA) for TSP. Firstly, in the DBSA algorithm, the information entropy matrix is constructed as a guide for generating new solutions. Each element of the information entropy matrix denotes the information entropy from city i to city j. The higher the information entropy, the larger the probability that a city will be visited. Secondly, each TSP path is represented as an array, and each element of the array represents a city index. Then according to the needs of the minus function proposed in this paper, each TSP path is transformed into a Boolean matrix which represents the relationship of edges. Third, the minus function is designed to evaluate the difference between two Boolean matrices. Based on the minus function and information entropy matrix, the birds’ position updating equations are redesigned to update the information entropy matrix without changing the original features. Then three TSP operators are proposed to generate new solutions according to the updated information entropy matrix. Finally, the performance of DBSA algorithm was tested on a large number of benchmark TSP instances. Experimental results show that DBSA algorithm is better or competitively outperforms many state-of-the-art metaheuristic algorithms.

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“…EDAs use a candidate solutions' spatial distribution probability model to replace conventional evolutionary operators such as crossover and mutation. UMDA [13] was proposed by German scholar Mühlenbein, which is one of EDAs and assumes the variable independent of each other. It can effectively solve high dimension problem.…”

Section: B Umdamentioning

confidence: 99%

Feature Selection on the Basis of Rough Set Theory and Univariate Marginal Distribution Algorithm

Wei¹,

Zhang²,

Liu³

et al. 2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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Abstract-Feature selection is an important preprocessing step in machine learning. The aim of feature selection is to find an optimal subset from original features that satisfies a criterion. Rough set theory (RST) is one of the most effective ways to solve feature selection problem, but RST is inefficient in large scale datasets. In order to solve this problem, in this paper, we proposed a novel feature selection algorithm RSUMDA on the basis of univariate marginal distribution algorithm. RST was used to obtain the significance of each feature as the original probability of UMDA and then UMDA was to search the optimal feature subset that using the number of the selected feature and the accuracy of the classifier as fitness function. Experimentation was carried out in 4 UCI datasets. The results showed that our algorithm could effectively reduce the number of the features, improve the accuracy of the classifier and quicken the convergence rate.

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Immune algorithm combined with estimation of distribution for traveling salesman problem

Cited by 28 publications

References 70 publications

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

Discrete Bird Swarm Algorithm Based on Information Entropy Matrix for Traveling Salesman Problem

Feature Selection on the Basis of Rough Set Theory and Univariate Marginal Distribution Algorithm

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