An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes

Huang, Jida; Gao, Liang; Li, Xinyu

doi:10.1016/j.asoc.2015.07.031

Cited by 106 publications

(15 citation statements)

References 32 publications

(30 reference statements)

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“…The performance of mTLBO is compared with other state of art algorithm like PSO, DE, ABC and GA and it is observed that addition of tutorial class concept improves the results of TLBO algorithm. To enhance local search ability and quality of solutions, Haung et al, have incorporated levy flight based teaching learning process for TLBO algorithm [27]. The proposed algorithm is adopted to solve several engineering optimization problems especially industrial optimization problems.…”

Section: Related Workmentioning

confidence: 99%

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Kumar

Dahiya²,

Malik³

et al. 2019

IJCAI

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This paper presents a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems. The performance of the TLBO algorithm depends on coordination of teacher phase and learner phase. It is noticed that sometimes performance of TLBO algorithm is affected due to lack of diversity in teacher and learner phases. In this work, a new variant of TLBO algorithm is proposed based on genetic crossover and mutation strategies. These strategies are inculcated in TLBO algorithm for improving its search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find the good learner. The effectiveness of the proposed algorithm is tested on several bench mark test functions of CEC'14. From simulation results, it is stated that the proposed algorithm provides more optimized results in comparison to same class of algorithms.Povzetek: V prispevku je opisan nov globalni optimizacijski algoritem na osnovi učenja optimizacije (TLBO).

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Section: Related Workmentioning

confidence: 99%

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Kumar

Dahiya²,

Malik³

et al. 2019

IJCAI

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“…This was impressively shown by Iqbal [10], where a knowledge-based system was used to capture relevant aspects of milling process in an efficient manner. Zhou et al [11] and Huang et al [12] did pick up this approach so as to build narrow intelligent machines for capturing essential process aspects. Those three contributions impressively show how soft computing leverages optimization to a whole new level of holisticity and therefore significance, even in light of complex manufacturing processes being hard to model via classical approaches.…”

Section: Brief Literature Review On Capturing Manufacturing Restrictimentioning

confidence: 99%

Enabling Composite Optimization through Soft Computing of Manufacturing Restrictions and Costs via a Narrow Artificial Intelligence

Schatz¹

2018

J. Compos. Sci.

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In industry, manufacturing has a huge imprint on structural design; which particularly holds for composites. This is caused by complex interaction of geometry, process parameters and material quantities e.g., fiber orientation. This interaction yields a wide variety of feasible designs, which severely differ in costs and structural performance, measured in mass, stiffness and strength. In order to cope most effectively with this complexity, this paper discusses a weak artificial intelligence, emulating human expertise on composite manufacturing. This approach is extended such that the used knowledge-based system is capable of providing a reason for having determined a certain level of manufacturing effort. Moreover, this extension also provides advice pointing into the direction of optimal improvement. These novelties may be used during designing, optimization and post-processing. These three cases are herein discussed by applying it onto an automotive structure.

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“…Obtain the data of power system and set the parameters of the algorithm Generate the initial population Evaluate tness for each individual; record the best solution Generate new solution using (16), (20) and …”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Generate new solution ( + 1) by the Lévy flight using (16), and selectively update the solution by (20) according to the discovery probability .…”

Section: Application Of Fcgcs To Solve the Opfmentioning

confidence: 99%

“…And some modified CS algorithms have been proposed and achieve better performance in various fields. Huang et al [16] presented a hybrid technique called teaching-learning-based cuckoo search (TLCS) to improve the final product quality during machining processes, which was applied to four different engineering optimization problems. Naik and Panda [17] proposed an adaptive cuckoo search (ACS) for face recognition.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Power Flow Using Gbest‐Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

Chen

Qiu

Zhang

et al. 2017

Mathematical Problems in Engineering

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The optimal power flow (OPF) is well-known as a significant optimization tool for the security and economic operation of power system, and OPF problem is a complex nonlinear, nondifferentiable programming problem. Thus this paper proposes a Gbestguided cuckoo search algorithm with the feedback control strategy and constraint domination rule which is named as FCGCS algorithm for solving OPF problem and getting optimal solution. This FCGCS algorithm is guided by the global best solution for strengthening exploitation ability. Feedback control strategy is devised to dynamically regulate the control parameters according to actual and specific feedback value in the simulation process. And the constraint domination rule can efficiently handle inequality constraints on state variables, which is superior to traditional penalty function method. The performance of FCGCS algorithm is tested and validated on the IEEE 30-bus and IEEE 57-bus example systems, and simulation results are compared with different methods obtained from other literatures recently. The comparison results indicate that FCGCS algorithm can provide high-quality feasible solutions for different OPF problems.

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An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes

Cited by 106 publications

References 32 publications

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Enabling Composite Optimization through Soft Computing of Manufacturing Restrictions and Costs via a Narrow Artificial Intelligence

Optimal Power Flow Using Gbest‐Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

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