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A reformative teaching-learning-based optimization algorithm for solving numerical and engineering design optimization problems

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SOFT COMPUTING 2020年第20期24卷 15889-15906页

作者： Li, Zhuang Zhang, Xiaotong Qin, Jingyan He, Jie Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Univ Sci & Technol Beijing Beijing Adv Innovat Ctr Mat Genome Engn Beijing Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing Peoples R China Univ Sci & Technol Beijing Beijing Key Lab Knowledge Engn Mat Sci Beijing Peoples R China

Teaching-learning-based optimization (TLBO) algorithm, which simulates the process of teaching-learning in the classroom, has been studied by many researchers, and a number of experiments have shown that it has great performance in solving optimization problems. However, it has an inherent origin bias in teacher phase and may fall into local optima for solving complex high-dimensional optimization problems. Therefore, an improved teaching method is proposed to eliminate the bias of converging toward the origin and enhance the ability of exploration during the convergence process. And a self-learning phase is presented to maintain the ability of exploration after convergence. Besides, a mutation phase is introduced to provide a good mixing ability among the population, preventing premature convergence. As a result, a reformative TLBO (RTLBO) algorithm with three modifications, an improved teaching method, a self-learning phase and a mutation phase, is proposed to significantly improve the performance of the TLBO algorithm. Ten unconstrained benchmark functions and three constrained engineering design problems are employed to evaluate the performance of the RTLBO algorithm. The results of the experiments show that the RTLBO algorithm is of excellent performance and better than, or at least comparable to, other available optimization algorithms in literature.

关键词： Evolutionary algorithm swarm intelligence based algorithm Unconstrained numerical optimization Constrained engineering optimization Teaching-learning-based optimization algorithm

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Spider Monkey Optimization algorithm for numerical optimization

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MEMETIC COMPUTING 2014年第1期6卷 31-47页

作者： Bansal, Jagdish Chand Sharma, Harish Jadon, Shimpi Singh Clerc, Maurice ABV Indian Inst Informat Technol & Management Gwalior India South Asian Univ New Delhi India

swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission-fusion social structure based animals. The animals which follow fission-fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission-fusion social structure based animals.

关键词： swarm intelligence based algorithm Optimization Fission-fusion social system Spider monkey optimization

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Self-adaptive position update in artificial bee colony

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2018年第4期9卷 802-810页

作者： Jadon, Shimpi Singh Sharma, Harish Tiwari, Ritu Bansal, Jagdish Chand ABV Indian Inst Informat Technol & Management Gwalior India Rajasthan Tech Univ Kota Rajasthan India South Asian Univ New Delhi India

Artificial bee colony (ABC), which is one of the leading swarm intelligence based algorithm, dominates other optimization algorithms in some fields but, it also has the drawbacks like premature convergence and slow convergence in the later stages due to unbalanced exploration and exploitation abilities. In this paper, we propose a novel variant of ABC, namely Self-adaptive Position update in ABC (SPABC), in which three position update strategies are incorporated in employed bee phase based on the fitness of the solutions. Each employed bee checks its fitness and accordingly adopts one of the position update strategies of standard ABC, Gbest guided ABC (GABC), and modified ABC (MABC). In this way, ABC with a set of solution update strategies of different characteristics can improve the quality of newly generated solutions and hence can improve the convergence speed of ABC. During solution generations, the suitable position update strategy is self-adapted according to the fitness of the solution. The performance of the SPABC is reported on the set of 15 real parameter benchmark test problems and is compared with standard ABC and its recent variants, namely BSFABC, GABC, and MABC.

关键词： Artificial bee colony swarm intelligence based algorithm Optimization Nature-inspired algorithm

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Global and Local Neighborhood based Particle swarm Optimization 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Chourasia, Shakti Sharma, Harish Singh, Manoj Bansal, Jagdish Chand Gurukul Inst Engn & Technol Kota India Rajasthan Tech Univ Kota India South Asian Univ New Delhi India

ISBN: (纸本)9789811307614;9789811307607

The particle swarm optimization (PSO) is one of the popular and simple to implement swarm intelligence based algorithms. To some extent, PSO dominates other optimization algorithms but prematurely converging to local optima and stagnation in later generations are some pitfalls. The reason for these problems is the unbalancing of the diversification and convergence abilities of the population during the solution search process. In this paper, a novel position update process is developed and incorporated in PSO by adopting the concept of the neighborhood topologies for each particle. Statistical analysis over 15 complex benchmark functions shows that performance of propounded PSO version is much better than standard PSO (PSO 2011) algorithm while maintaining the cost-effectiveness in terms of function evaluations.

关键词： swarm intelligence based algorithm Nature-inspired algorithm Neighborhood topology Optimization

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Intelligent Neighbourhood Teaching Learning based Optimization algorithm

Intelligent Neighbourhood Teaching Learning Based Optimizati...

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IEEE International Conference on Computing, Communication and Automation (ICCCA)

作者： Singh, Geetanjali Sharma, Nirmala Sharma, Harish Rajasthan Tech Univ Kota India

ISBN: (纸本)9781509064717

Teaching Learning based Optimization algorithm (TLBOA) is an efficient approach of dealing with linear, nonlinear and multidimensional optimization problems. It depicts the classroom behaviour of teachers and students (learners). The algorithm is divided in two phases, namely Teacher phase and Learner phase. The position update process of learner phase is based on the difference vector of two randomly selected solutions, which improves the diversification ability of the algorithm. Further, in case of high step size, the solutions may skip the global optima. Therefore, to improve the exploitation ability and to control the step size, a new phase called as intelligent phase inspired from the Onlooker bee phase of Artificial Bee Colony optimization algorithm, is incorporated with the TLBO. In the propounded phase, intelligent solutions will be given more chances in participation of the position update process. This will leads to high convergence in the swarm. The performance of this propounded algorithm is calculated over 15 benchmark functions and compared with other state of art optimization algorithms namely TLBO, Gravitational Search algorithm (GSA), and Grey Wolf Optimizer (GWO).

关键词： Teaching Learning based Optimization Intelligent Phase swarm intelligence based algorithm Optimization

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