scholarly journals Solution of Economically Load Scheduling Problem using Teaching Learning and Bio-Geography Based Hybrid Optimization Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 4284-4293
Keyword(s):  
Optimization Algorithm ◽  
Learning Algorithm ◽  
Biological Species ◽  
Linear Constraints ◽  
Ramp Rate ◽  
Load Demand ◽  
Hybrid Optimization Algorithm ◽  
Teaching Learning ◽  
Rate Limit ◽  
Valve Point Loading Effect

The article presents a new optimization algorithm view namely hybridization of teaching learning (TLBO) and biogeography based (BBO) optimization algorithm used to solve the convex economic dispatch (ED) problem with non-linear constraints like ramp rate limit, valve point loading effect etc. Hybridization of TLBO and BBO is the mixed combination of superior properties of TLBO and BBO. Teaching learning algorithm (TLBO) is based on teacher learner relationship in class and bio-geography algorithm (BBO) is based on geographical representation of biological species. The main goal of ED is to allocate power allocation economically meeting load demand. The proposed algorithm is tested for 13-unit, 15-unit, 40-unit and 140 unit systems. For proving superiority properties of proposed algorithm, obtained result are compared with recent algorithm. It gives optimum fuel cost compared to other optimization algorithm.

Improved Teaching-Learning-Based Optimization Algorithm and its Application in PID Parameter Optimization

2019 ◽  
Vol 13 (2) ◽  
pp. 1-17
Author(s):  
Fahui Gu ◽  
Wenxiang Wang ◽  
Luyan Lai
Keyword(s):  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Adaptive Learning ◽  
Teaching And Learning ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Global Search ◽  
Opposition Based Learning ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges slowly in the late period and makes it too early to mature. To overcome these shortcomings, this article proposes a dual-population co-evolution teaching and learning optimization algorithm (DPCETLBO) in which adaptive learning factors and a multi-parent non-convex hybrid elite strategy are introduced for a population with high fitness values to improve the convergence speed of the algorithm, while an opposition-based learning algorithm with polarization is introduced for a population with lower fitness values to improve the global search ability of the algorithm. In a proportion integration differentiation (PID) parameter optimization experiment, the simulation results indicate that the convergence of the DPCETLBO algorithm is fast and precise, and its global search ability is superior to those of the TLBO, ETLBO and PSO algorithms.

scholarly journals Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Simplex Algorithm ◽  
Local Optimum ◽  
Hybrid Optimization Algorithm ◽  
Multimodal Function ◽  
Remote Laboratories ◽  
Remote Lab ◽  
Timetable Problem

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

Optimization Design of Drawbead in Drawing Tools of Autobody Cover Panel

2002 ◽  
Vol 124 (2) ◽  
pp. 278-285 ◽  
Author(s):  
Gang Liu ◽  
Zhongqin Lin ◽  
Youxia Bao
Keyword(s):  
Optimization Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Experimental Results ◽  
Hybrid Optimization ◽  
Force Model ◽  
Hybrid Optimization Algorithm ◽  
Design Plan ◽  
Formed Part ◽  
Restraining Force

In the tooling design of autobody cover panels, design of drawbead will affect the distribution of drawing restraining force along mouth of dies and the relative flowing velocity of the blank, and consequently, will affect the distributions of strain and thickness in a formed part. Therefore, reasonable design of drawbead is the key point of cover panels’ forming quality. An optimization design method of drawbead, using one improved hybrid optimization algorithm combined with FEM software, is proposed in this paper. First, we used this method to design the distribution of drawbead restraining force along the mouth of a die, then the actual type and geometrical parameters of drawbead could be obtained according to an improved drawbead restraining force model and the improved hybrid optimization algorithm. This optimization method of drawbead was used in designing drawing tools of an actual autobody cover panel, and an optimized drawbead design plan has been obtained, by which deformation redundancy was increased from 0% under uniform drawbead control to 10%. Plastic strain of all area of formed part was larger than 2% and the minimum flange width was larger than 10 mm. Therefore, not only better formability and high dent resistance were obtained, but also fine cutting contour line and high assembly quality could be obtained. An actual drawing part has been formed using the optimized drawbead, and the experimental results were compared with the simulating results in order to verify the validity of the optimized design plan. Good agreement of thickness on critical areas between experimental results and simulation results proves that the optimization design method of drawbead could be successfully applied in designing actual tools of autobody cover panels.

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