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A hybrid teaching-learning slime mould algorithm for global optimization and reliability-based design optimization problems

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NEURAL COMPUTING & APPLICATIONS 2022年第19期34卷 16617-16642页

作者： Zhong, Changting Li, Gang Meng, Zeng Dalian Univ Technol State Key Lab Struct Analyses Ind Equipment Dept Engn Mech Dalian 116024 Peoples R China Hefei Univ Technol Sch Civil Engn Hefei 230009 Peoples R China

Slime mould algorithm (SMA) is a novel metaheuristic algorithm with good performance for optimization problems, but it may encounter premature or low accuracy in complex optimization problems. This paper presents a hybrid SMA using teaching-learning based optimization (TLBO), called TLSMA, for solving global optimization and reliability-based design optimization (RBDO) problems. The key point of TLSMA is to combine the capacities of exploration and exploitation from SMA and TLBO, which can enhance the convergence ability of SMA. Moreover, the TLSMA is extended for solving RBDO problems under probabilistic constraints, which are handled by the adaptive chaos control method. The proposed algorithm is tested by a series of experiments with two parts. First, TLSMA is verified by 24 well-known benchmark optimization problems with unimodal and multimodal functions, and is compared with several state-of-the-art metaheuristic algorithms. The results of benchmark optimization problems show that TLSMA outperforms PSO, BBO, GWO, WOA, SSA, TLBO and SMA. Then, TLSMA-RBDO is tested by five RBDO problems, including a numerical and four engineering problems. The results illustrate that the proposed algorithm has high performance in the RBDO problems, which is significantly superior to the compared algorithms.

关键词： Reliability-based design optimization teaching-learning-based optimization Slime mould algorithm Adaptive chaos control method

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teaching-learning based optimization Algorithm based on Course by Course Improvement 11

Teaching-Learning Based Optimization Algorithm based on Cour...

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11th International Conference on Computational Intelligence and Security (CIS)

作者： Qu, Dapeng Liu, Shuwen Zhang, Di Wang, Jun Gao, Chengxi Liaoning Univ Coll Informat Shenyang Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781467386609

teaching-learning-based optimization (TLBO) algorithm is a new intelligence algorithm. It takes evaluation scheme which updates learners' grade totally in solving multidimensional function optimization problems, thus decreases the convergence speed. Moreover, the interference phenomena among dimensions would influence the quality of solutions. While all existing improvement strategies on TLBO introduce new parameters, thus destroy the simplicity of TLBO. To solve the above problems, a TLBO algorithm based on Course by course Improvement (TLBOCI) was proposed. This algorithm takes strategy which updates learners' grade based on course by course improvement in teaching and learning phase in each iteration, and this strategy combines an updated value of one course with the values of other courses into a new solution. The results of four classic test functions show that the TLBOCI algorithm could outperform TLBO in quality of solutions and convergence speed under the condition of not introducing new parameters thus keeping the simplicity of TLBO.

关键词： intelligence algorithm teaching-learning-based optimization multi-dimensional function optimization convergence speed

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teaching-learning based optimization with Crossover Operation 27

Teaching-Learning Based Optimization with Crossover Operatio...

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27th Chinese Control and Decision Conference (CCDC)

作者： Zhao, Xiu-hong Anshan Normal Univ Dept Phys Anshan 114005 Peoples R China

ISBN: (纸本)9781479970162

This paper developed a new variant of teaching-learning-based optimization (TLBO), called teaching-learning-based optimization with Crossover (TLBOC), for improving the performance of TLBO. The TLBOC incorporated the conventional crossover operation of differential evolution (DE) algorithm into teaching phases, which aims at balancing local and global searching effectively. Moreover, an estimation of distribution operation is used to predict a learning elitist. The learning elitist helps to boost learning efficiency of each student in learning phase. The performance of TLBOC is assessed for solving global unconstrained optimization functions with different characteristics. Compared to the TLBO and several other promising heuristic methods, numerical results reveal that the TLBOC has better optimization performance.

关键词： teaching-learning-based optimization Global searching capability Crossover Performance

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Improved teaching-learning based optimization for Global optimization Problems 34

Improved Teaching-Learning Based Optimization for Global Opt...

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34th Chinese Control Conference (CCC)

作者： Zhao, Xiu-hong Anshan Normal Univ Dept Phys Anshan 114005 Peoples R China

ISBN: (纸本)9789881563897

teaching-learning-based optimization (TLBO) is a new population-based meta-heuristic algorithm. In this paper, a new variant of TLBO-Improved teaching-learning-based optimization (ITLBO) is developed for solving global optimization problems. The proposed ITLBO incorporates the position updating operation of swarm intelligent algorithm into different phases and aims at effectively balancing the local and global searching. Gaussian perturbation strategy is presented to prevent TLBO algorithm from trapping into local minima. Moreover, opposition-based learning technique is employed in learning phase to expand the exploration space. Experimental results reveal that ITLBO appear to enhance the solution accuracy and quality compared to TLBO and other promising heuristic methods.

关键词： teaching-learning-based optimization global optimization accuracy global searching

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Disassembly sequence planning using a Simplified teaching-learning-based optimization algorithm

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ADVANCED ENGINEERING INFORMATICS 2014年第4期28卷 518-527页

作者： Xia, Kai Gao, Liang Li, Weidong Chao, Kuo-Ming Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Coventry Univ Fac Engn & Comp Coventry CV1 5FB W Midlands England

Disassembly Sequence Planning (DSP) is a challenging NP-hard combinatorial optimization problem. As a new and promising population-based evolutional algorithm, the teaching-learning-based optimization (TLBO) algorithm has been successfully applied to various research problems. However, TLBO is not capable or effective in DSP optimization problems with discrete solution spaces and complex disassembly precedence constraints. This paper presents a Simplified teaching-learning-based optimization (STLBO) algorithm for solving DSP problems effectively. The STLBO algorithm inherits the main idea of the teaching-leaming-based evolutionary mechanism from the TLBO algorithm, while the realization method for the evolutionary mechanism and the adaptation methods for the algorithm parameters are different. Three new operators are developed and incorporated in the STLBO algorithm to ensure its applicability to DSP problems with complex disassembly precedence constraints: i.e., a Feasible Solution Generator (FSG) used to generate a feasible disassembly sequence, a teaching Phase Operator (TPO) and a learning Phase Operator (LPO) used to learn and evolve the solutions towards better ones by applying the method of precedence preservation crossover operation. Numerical experiments with case studies on waste product disassembly planning have been carried out to demonstrate the effectiveness of the designed operators and the results exhibited that the developed algorithm performs better than other relevant algorithms under a set of public benchmarks. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Disassembly Disassembly sequence planning Meta-heuristics teaching-learning-based optimization Simplified teaching-learning-based optimization

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Optimal PMU Placement by teaching-learning based optimization Algorithm 39

Optimal PMU Placement by Teaching-Learning Based Optimizatio...

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39th National Systems Conference (NSC)

作者： Raj, Akhil Venkaiah, Chintham Indian Inst Technol Dept Elect Engn Bombay Maharashtra India NIT Warangal Dept Elect Engn IEEE Warangal Andhra Pradesh India

ISBN: (纸本)9781467368292

In this paper teaching-learning-based optimization Algorithm (TLBO) is presented for solving the problem of placement of phasor measurement units (PMU) optimally in a power system network for complete observability. The TLBO algorithm enables optimal PMU placement by zero injection measurements and also by not including zero injection measurements. The algorithm has been tested on standard test systems such as IEEE 14-bus, IEEE 30-bus, IEEE 57-bus and the results are contrasted with other optimization algorithms like Genetic Algorithm and Binary PSO.

关键词： Phasor measurement units Observability Optimal placement teaching-learning-based optimization

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A comparative study of a teaching-learning-based optimization algorithm on multi-objective unconstrained and constrained functions

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2014年第3期26卷 332-346页

作者： Rao, R. Venkata Waghmare, G. G. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

Multi-objective optimization is the process of simultaneously optimizing two or more conflicting objectives subject to certain constraints. Real-life engineering designs often contain more than one conflicting objective function, which requires a multi-objective approach. In a single-objective optimization problem, the optimal solution is clearly defined, while a set of trade-offs that gives rise to numerous solutions exists in multi-objective optimization problems. Each solution represents a particular performance trade-off between the objectives and can be considered optimal. In this paper, the performance of a recently developed teaching-learning-based optimization (TLBO) algorithm is evaluated against the other optimization algorithms over a set of multi-objective unconstrained and constrained test functions and the results are compared. The TLBO algorithm was observed to outperform the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems. (C) 2013 King Saud University. Production and hosting by Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Multi-objective optimization Unconstrained and constrained benchmark functions

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optimization of job shop scheduling problems using teaching-learning-based optimization algorithm

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OPSEARCH 2014年第4期51卷 545-561页

作者： Keesari, H. S. Rao, R. V. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

A job shop scheduling problem is one of the most difficult NP hard combinatorial optimization problems. In a job shop scheduling problem (JSSP), there are n jobs that should be processed on m machines. Each job consists of a predetermined sequence of task operations, each of which needs to be processed without interruption for a given period of time on a given machine. Tasks of the same job cannot be processed concurrently. In recent years, optimization algorithms such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS), ant colony optimization (ACO) particle swarm optimization (PSO) and artificial bee colony (ABC) have played a significant role in solving small-scale job shop scheduling problems. However, when the problem size grows, metaheuristic algorithms usually take excessive time to converge. In this study, a recently developed teaching-learning- based optimization (TLBO) method is proposed to solve the job shop scheduling problems to minimize the makespan. The proposed algorithm is tested on 58 job shop scheduling bench mark problems from OR Library and results are compared with the results obtained by using the other algorithms. It is concluded that the TLBO algorithm can be effectively used for job shop scheduling problems.

关键词： Job shop scheduling Benchmark problems teaching-learning-based optimization

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A New teaching-learning-based Chicken Swarm optimization Algorithm

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SOFT COMPUTING 2020年第7期24卷 5313-5331页

作者： Deb, Sanchari Gao, Xiao-Zhi Tammi, Kari Kalita, Karuna Mahanta, Pinakeswar Indian Inst Technol Ctr Energy Gauhati India Univ Eastern Finland Sch Comp Joensuu Finland Aalto Univ Dept Mech Engn Espoo Finland Indian Inst Technol Dept Mech Engn Gauhati India Natl Inst Technol Dept Mech Engn Yupia Arunachal Prade India

Chicken Swarm optimization (CSO) is a novel swarm intelligence-based algorithm known for its good performance on many benchmark functions as well as real-world optimization problems. However, it is observed that CSO sometimes gets trapped in local optima. This work proposes an improved version of the CSO algorithm with modified update equation of the roosters and a novel constraint-handling mechanism. Further, the work also proposes synergy of the improved version of CSO with teaching-learning-based optimization (TLBO) algorithm. The proposed ICSOTLBO algorithm possesses the strengths of both CSO and TLBO. The efficacy of the proposed algorithm is tested on eight basic benchmark functions, fifteen computationally expensive benchmark functions as well as two real-world problems. Further, the performance of ICSOTLBO is also compared with a number of state-of-the-art algorithms. It is observed that the proposed algorithm performs better than or as good as many of the existing algorithms.

关键词： Algorithm Benchmark Chicken Swarm optimization Function Hybrid teaching-learning-based optimization

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Improved teaching-learning based optimization for Global optimization Problems

Improved Teaching-Learning Based Optimization for Global Opt...

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第三十四届中国控制会议

作者： Xiu-hong ZHAO Physics Department Anshan Normal University

ISBN: (纸本)9781467374439

teaching-learning-based optimization(TLBO) is a new population-based meta-heuristic algorithm. In this paper, a new variant of TLBO-Improved teaching-learning-based optimization(ITLBO) is developed for solving global optimization problems. The proposed ITLBO incorporates the position updating operation of swarm intelligent algorithm into different phases and aims at effectively balancing the local and global searching. Gaussian perturbation strategy is presented to prevent TLBO algorithm from trapping into local minima. Moreover, opposition-based learning technique is employed in learning phase to expand the exploration space. Experimental results reveal that ITLBO appear to enhance the solution accuracy and quality compared to TLBO and other promising heuristic methods.

关键词： teaching-learning-based optimization global optimization accuracy global searching

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