scholarly journals Constrained Optimization Based on Hybridized Version of Superiority of Feasibility Solution Strategy

2021 ◽  
Author(s):  
Wali Khan ◽  
Faiz Ur Rehaman ◽  
Habib Shah
Keyword(s):  
Decision Making ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Population Based ◽  
Stochastic Algorithm ◽  
Weighted Mean ◽  
Hybrid Superiority ◽  
Decision Making Factors ◽  
Two Phases ◽  
Teaching Learning

Abstract Teaching learning based optimization (TLBO) is a stochastic algorithm which was first proposed for unconstrained optimization problems. It is population based, nature-inspired, and meta-heuristic that imitates teaching learning process. It has two phases, teacher and learner. In teacher phase, the teacher who is well-learned person transfers his/her knowledge to the learners to raise their grades/results; while in learner phase, learners/pupils learn and refine their knowledge through mutual interconnection. To solve constrained optimization problems (COPs) through TLBO we need to merge it with some constraint handling technique (CHT). Superiority of feasibility (SF) is a concept for making CHTs, existed in different forms based on various decisive factors. Most commonly used decision making factors in SF are number of constraints violated (NCV) and weighted mean (WM) values for comparing solutions. In this work, SF based on number of constraints violated (NCVSF) and weighted mean (WMSF) are incorporated in the framework of TLBO. These are tested upon CEC-2006 constrained suit with the remark that single factor used for the decision making of winner is not a wise idea. Mentioned remark leads us to made a single CHT that carries the capabilities of both discussed CHTs. It laid the foundation of hybrid superiority of feasiblity (HSF); where NCV and WM factors are combined with giving dominance to NCV over WM. In current research three constrained versions of TLBO are formulated by the name NCVSF-TLBO, WMSF-TLBO, and HSF-TLBO; while implanting NCVSF, WMSF, and HSF in the framework of TLBO, respectively. These constrained versions of TLBO are evaluated on CEC-2006 with the remarks that HSF-TLBO got prominent and flourishing status among these.

scholarly journals An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-29 ◽  
Author(s):  
Shouheng Tuo ◽  
Longquan Yong ◽  
Tao Zhou
Keyword(s):  
High Dimension ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Population Diversity ◽  
Random Mutation ◽  
Complex Optimization ◽  
Teaching Learning

Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.

scholarly journals Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
B. Thamaraikannan ◽  
V. Thirunavukkarasu
Keyword(s):  
Differential Operator ◽  
Optimization Problems ◽  
Population Based ◽  
Optimal Solutions ◽  
Hollow Shaft ◽  
Optimization Task ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Mechanical Components ◽  
Teaching Learning

This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO) algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components) problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.

Decision-making model based multilayer perceptrons for estimation of optimum design properties for truss structure

2021 ◽  
Vol 7 (4) ◽  
pp. 170
Author(s):  
Melda Yücel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Decision Making ◽  
Optimum Design ◽  
Structural Engineering ◽  
Structural Model ◽  
Population Based ◽  
Multilayer Perceptrons ◽  
Optimal Section ◽  
Optimum Model ◽  
Teaching Learning ◽  
Decision Making Model

Many branches of the structural engineering discipline have many problems, which require the generating an optimum model for beam-column junction area reinforcement, weight lightening for members such a beam, column, slab, footing formed as reinforced concrete, steel, composite, and so on, cost arrangement for any construction, etc. With this direction, in the current study, a structural model as a 5-bar truss is handled to provide an optimum design by determining the fittest areas of bar sections. It is aimed that the total bar length is minimized through population-based metaheuristic algorithm as teaching-learning-based optimization (TLBO). Following, the decision-making model is developed via multilayer perceptrons (MLPs) by performing an estimation application to enable directly foreseen of the optimal section areas and total length of bars, besides, the approximation and correlation success are evaluated via some metrics. Thus, determination of the real optimal results of unknown and not-tested designs can be realized with this model in a short and effective time.

scholarly journals Hybrid Biogeography Based Optimization for Constrained Numerical and Engineering Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Zengqiang Mi ◽  
Yikun Xu ◽  
Yang Yu ◽  
Tong Zhao ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Population Based ◽  
Global Optimum ◽  
Engineering Optimization ◽  
Mutation Operator ◽  
Random Mutation ◽  
Promising Solution ◽  
Population Based Algorithm

Biogeography based optimization (BBO) is a new competitive population-based algorithm inspired by biogeography. It simulates the migration of species in nature to share information. A new hybrid BBO (HBBO) is presented in the paper for constrained optimization. By combining differential evolution (DE) mutation operator with simulated binary crosser (SBX) of genetic algorithms (GAs) reasonably, a new mutation operator is proposed to generate promising solution instead of the random mutation in basic BBO. In addition, DE mutation is still integrated to update one half of population to further lead the evolution towards the global optimum and the chaotic search is introduced to improve the diversity of population. HBBO is tested on twelve benchmark functions and four engineering optimization problems. Experimental results demonstrate that HBBO is effective and efficient for constrained optimization and in contrast with other state-of-the-art evolutionary algorithms (EAs), the performance of HBBO is better, or at least comparable in terms of the quality of the final solutions and computational cost. Furthermore, the influence of the maximum mutation rate is also investigated.

scholarly journals Application of Teaching Learning Based Optimization in antenna designing

2015 ◽  
Vol 4 (1) ◽  
pp. 68 ◽  
Author(s):  
S. Dwivedi ◽  
V. Mishra ◽  
Y. Kosta
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Analytic Hierarchy ◽  
Weighted Sum Method ◽  
Order Preference ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Hierarchy Process

Numerous optimization techniques are studied and applied on antenna designs to optimize various performance parameters. Authors used many Multiple Attributes Decision Making (MADM) methods, which include, Weighted Sum Method (WSM), Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Analytic Hierarchy Process (AHP), ELECTRE, etc. Of these many MADM methods, TOPSIS and AHP are more widely used decision making methods. Both TOPSIS and AHP are logical decision making approaches and deal with the problem of choosing an alternative from a set of alternatives which are characterized in terms of some attributes. Analytic Hierarchy Process (AHP) is explained in detail and compared with WSM and WPM. Authors fi- nally used Teaching-Learning-Based Optimization (TLBO) technique; which is a novel method for constrained antenna design optimization problems.

scholarly journals Settings-Free Hybrid Metaheuristic General Optimization Methods

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1092
Author(s):  
Héctor Migallón ◽  
Akram Belazi ◽  
José-Luis Sánchez-Romero ◽  
Héctor Rico ◽  
Antonio Jimeno-Morenilla
Keyword(s):  
Control Parameter ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Parameter Tuning ◽  
Optimization Methods ◽  
Population Based ◽  
Hybrid Algorithms ◽  
Hybridization Technique ◽  
Control Parameters ◽  
Teaching Learning

Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed.

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