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An effective PSO-TLBO algorithm for multi-objective optimization

An effective PSO-TLBO algorithm for multi-objective optimiza...

IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Cheng, Tingli Chen, Minyou Fleming, Peter J. Yang, Zhile Gan, Shaojun Chongqing Univ Sch Elect Engn Chongqing Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

ISBN: (纸本)9781509006229

In this study, we present an effective hybrid algorithm based on particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), called PSO-TLBO, to solve multi-objective optimization (MOO) problems. In PSO-TLBO, teacher phase and learner phase in TLBO are incorporated to a PSO to improve the exploration and exploitation ability. Another feature of PSO-TLBO is the improved circular crowed sorting (CCS) to truncate the external archive which stores the nondominated solutions found during the search. Moreover, the polynomial mutation is used only when the velocities of all solutions are very small. The performance of PSO-TLBO algorithm for MOO is tested on ZDT and DTLZ benchmarks functions and compared with some powerful MOO algorithms in terms of both convergence and spread performance. Also, the contribution of CCS is compared with crowded sorting in NSGA-II. Comparison results reveal the powerful ability of PSO-TLBO to obtain remarkable solutions in terms of inverted generational distance (IGD) metrics.

关键词： Multi-objective optimization particle swarm optimization teaching-learning-based optimization

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A hybrid evolutionary algorithm for solving function optimization problems 12

A hybrid evolutionary algorithm for solving function optimiz...

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12th International Conference on Computational Intelligence and Security (CIS)

作者： Gu, Fahui Li, Kangshun Liu, Yue South China Agr Univ Coll Math & Informat Guangzhou 510006 Guangdong Peoples R China Jiangxi Appl Technol Vocat Coll Sch Informat Engn Ganzhou 341000 Peoples R China

ISBN: (纸本)9781509048403

This paper proposes a hybrid evolutionary algorithm based on DE and TLBO (HDT). The HDT algorithm independently evolved the two sub populations of DE and TLBO to preserve the diversity of population. In every generation of evolution, we first get a composite individual from current best individuals of the two sub populations, then we use search guide of composite individual to remain the variety of population and to achieve complementary advantages of DE and TLBO. Experiments have been conducted on 13 benchmark problems. Empirical results fully demonstrate the effectiveness and competitiveness of the proposed hybrid evolutionary algorithm.

关键词： global optimization evolutionary algorithm differential evolutionary teaching-learning-based optimization

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Parameter estimation of nonlinear chaotic system by improved TLBO strategy

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SOFT COMPUTING 2016年第12期20卷 4965-4980页

作者： Zhang, Hongjun Li, Baozhu Zhang, Jun Qin, Yuanhui Feng, Xiaoyi Liu, Bo Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Syst Engn Res Inst Beijing 100094 Peoples R China

Estimation of parameters of chaotic systems is a subject of substantial and well-developed research issue in nonlinear science. From the viewpoint of optimization, parameter estimation can be formulated as a multi-modal constrained optimization problem with multiple decision variables. This investigation makes a systematic examination of the feasibility of applying a newly proposed population-based optimization method labeled here as teaching-learning-based optimization (TLBO) to identify the unknown parameters for a class of chaotic system. The preliminary test demonstrates that despite its global fast coarse search capability, teaching-learning-based optimization often risks getting prematurely stuck in local optima. To enhance its fine (local) searching performance of TLBO, Nelder-Mead simplex algorithm-based local improvement is incorporated into TLBO so as to continually search for the global optima through the reflection, expansion, contraction, and shrink operators. Working with the well-established Lorenz system, we assess the effectiveness and efficiency of the proposed improved TLBO strategy. The empirical results indicate the success of the proposed hybrid approach in which the global exploration and the local exploitation are well balanced, providing the best solutions for all instances used over other state-of-the-art metaheuristics for chaotic identification in literature, including particle swarm optimization, genetic algorithm, and quantum-inspired evolutionary algorithm.

关键词： Parameter estimation System identification Chaotic system teaching-learning-based optimization Nelder-Mead simplex algorithm Memetic algorithm

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Optimal targeting of nonlinear chaotic systems using a novel evolutionary computing strategy

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KNOWLEDGE-based SYSTEMS 2016年第Sep.1期107卷 261-270页

作者： Wang, Yudong Feng, Xiaoyi Lyu, Xin Li, Zhengyang Liu, Bo Sci & Technol Space Phys Lab Beijing Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China

Control of chaotic systems to given targets is a subject of substantial and well-developed research issue in nonlinear science, which can be formulated as a class of multi-modal constrained numerical optimization problem with multi-dimensional decision variables. This investigation elucidates the feasibility of applying a novel population-based metaheuristics labeled here as teaching-learning-based optimization to direct the orbits of discrete chaotic dynamical systems towards the desired target region. Several consecutive control steps of small bounded perturbations are made in the teaching-learning-based optimization strategy to direct the chaotic series towards the optimal neighborhood of the desired target rapidly, where a conventional controller is effective for chaos control. Working with the dynamics of the well-known Henan as well as Ushio discrete chaotic systems, we assess the effectiveness and efficiency of the teaching-learning-based optimization based optimal control technique, meanwhile the impacts of the core parameters on performances are also discussed. Furthermore, possible engineering applications of directing chaotic orbits are discussed. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Chaos control Chaotic dynamics optimization Computational intelligence teaching-learning-based optimization

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Is a comparison of results meaningful from the inexact replications of computational experiments?

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SOFT COMPUTING 2016年第1期20卷 223-235页

作者： Crepinsek, Matej Liu, Shih-Hsi Mernik, Luka Mernik, Marjan Univ Maribor SLO-2000 Maribor Slovenia Calif State Univ Fresno Fresno CA 93740 USA CALTECH Pasadena CA 91125 USA

The main objective of this paper is to correct the unreasonable and inaccurate criticism to our previous experiments using teaching-learning-based optimization algorithm and to quantify the amount of error that may arise due to incorrect counting of fitness evaluations. It is shown that inexact experiment replication should be avoided in comparisons between meta-heuristic algorithms whenever possible. Otherwise, an inexact replication and margin of error should be explicitly reported.

关键词： teaching-learning-based optimization Algorithm comparison Replication of experiments

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GPU Implementation of TLBO Algorithm to Test Constrained and Unconstrained Benchmark Functions

GPU Implementation of TLBO Algorithm to Test Constrained and...

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International Conference on Computing, Analytics and Security Trends (CAST)

作者： Ramanlal, Shah Sujeeta Krishna, Shinde Santaji Bharati Vidyapeeths Coll Engn Dept Comp Sceince & Engn Kolhapur Maharashtra India Bharati Vidyapeeths Coll Engn Dept Informat Technol Kolhapur Maharashtra India

ISBN: (纸本)9781509013388

The objective function may have many local optima whereas the designer is interested in the global optimum. The classical methods (e.g. Gradient methods) were cannot be handled by such problems. They only compute the local optima. So there remains a need for efficient and effective optimization methods for mechanical design problems. The Continuous research is being conducted in this field by different optimization techniques. The nature-inspired heuristic optimization methods are proving to be better than deterministic methods. In this paper, the Performance of algorithm on unconstrained and constrained benchmark functions is tested by running these algorithms sequentially. The main motivation of using parallel computing is to improve the performance of these algorithms parallely. GPGPU is applicable where data parallelism and independency is possible, a good implementation on a GPGPU can achieve more than 100 times better speedup over sequential execution. CUDA implementation proves better in the evolutionary algorithm not only regarding of speed up but also in convergence time.

关键词： teaching-learning-based optimization Compute unified device architecture General purpose graphics processing unit constrained benchmark function and unconstrained enchmark functions

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Applying the Modified TLBO Algorithm to Solve the Unit Commitment Problem

Applying the Modified TLBO Algorithm to Solve the Unit Commi...

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World Automation Congress (WAC)

作者： Khazaei, Peyman Dabbaghjamanesh, Morteza Kalantarzadeh, Ali Mousavi, Hasan Shiraz Univ Technol Elect & Comp Engn Dept Shiraz Iran Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA Niroosazeh Co TIS Smart Home Shiraz Iran

ISBN: (纸本)9781889335513

In the day-ahead power systems scheduling, system operators formulate and solve the unit commitment (UC) problem to determine ON/OFF status and power dispatch of the generating units. Although various methods have been presented to solve the UC problem, it is a mixed-integer optimization problem which is hard to find its global optimum solution. In this paper, the new teaching-learning-based optimization (TLBO) technique, which is an evolutionary algorithm, is employed to solve the unit commitment problem. The application of the TLBO on the UC include two phases, teaching and learning phases. A set of population is defined for the UC problem solution. Then, an iterative teaching and learning procedure is implemented to determine the best UC population which has the minimum operating cost while all system and units' constraints, including spinning reserve constraint, are satisfied. Simulation studies show the convergence speed and effeteness of the proposed TLBO algorithm to solve the UC problem. The proposed algorithm is compared with several existing methods.

关键词： Power system operation unit commitment teaching-learning-based optimization evolutionary algorithm

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Combined size and shape optimization of structures with a new meta-heuristic algorithm

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APPLIED SOFT COMPUTING 2015年 28卷 250-258页

作者： Dede, Tayfun Ayvaz, Yusuf Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey Yildiz Tech Univ Dept Civil Engn Istanbul Turkey

In this study, a new meta-heuristic algorithm called teaching-learning-based optimization (TLBO) is used for the size and shape optimization of structures. The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. The cross-sectional areas of the bar element and the nodal coordinates of the structural system are the design variables for size and shape optimization, respectively. Displacement, allowable stress and the Euler buckling stress are taken as the constraint for the problem considered. Some truss structures are designed by using this new algorithm to show the efficiency of the TLBO algorithm. The results obtained from this study are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in combined size and shape optimization of the structures. (C) 2014 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Size optimization Shape optimization Truss structure

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A hybrid ensemble method based on double disturbance for classifying microarray data

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BIO-MEDICAL MATERIALS AND ENGINEERING 2015年第Sup1期26卷 S1961-S1968页

作者： Chen, Tao Xue, Huifeng Hong, Zenglin Cui, Man Zhao, Hui Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723000 Shaanxi Peoples R China

Microarray data has small samples and high dimension, and it contains a significant amount of irrelevant and redundant genes. This paper proposes a hybrid ensemble method based on double disturbance to improve classification performance. Firstly, original genes are ranked through reliefF algorithm and part of the genes are selected from the original genes set, and then a new training set is generated from the original training set according to the previously selected genes. Secondly, D bootstrap training subsets are produced from the previously generated training set by bootstrap technology. Thirdly, an attribute reduction method based on neighborhood mutual information with a different radius is used to reduce genes on each bootstrap training subset to produce new training subsets. Each new training subset is applied to train a base classifier. Finally, a part of the base classifiers are selected based on the teaching-learning-based optimization to build an ensemble by weighted voting. Experimental results on six benchmark cancer microarray datasets showed proposed method decreased ensemble size and obtained higher classification performance compared with Bagging, AdaBoost, and Random Forest.

关键词： Microarray data bagging reliefF neighborhood mutual information teaching-learning-based optimization

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Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques - a Comparative Study

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2015年第3期61卷 176-186页

作者： Pare, Vikas Agnihotri, Geeta Krishna, Chimata Maulana Azad Natl Inst Technol Bhopal India

The machining of aluminium metal matrix composites in CNC high speed conditions is significant because such composites have diverse applications in the aeronautics industry. Because that industry requires high quality outcomes, the prediction of surface roughness, which depends on input process parameters, assumes significance in the maintaining quality of products. Even though many researchers have worked in the area of conventional machining very few of them have explored optimization techniques, such as teaching-learning-based optimization (TLBO) and gravitational search algorithms (GSA) in high speed environments. In this research, an attempt is made to determine the optimum machining conditions for the end-milling of composite materials using GSA. Input process parameters, such as cutting speed, feed, the depth of cut and the step-over ratio are taken as independent variables, and surface roughness is taken as dependent variable. Experiments were conducted on Al2O3 + SIC metal matrix composite by considering selected variations in the input process parameters. Surface roughness is measured in each of cases, and the required data is obtained for further analysis. An empirical relationship is established between dependent and independent variables in the form of linear and non-linear regression equations, and the results are analysed. The results showed that GSA gives better results for surface roughness when compared to the genetic algorithm, simulated annealing and TLBO methods. An additional set of experiments was conducted to validate the results obtained.

关键词： high-speed machining metal matrix composite surface roughness gravitational search algorithms teaching-learning-based optimization

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