Monitor system and Gaussian perturbation teaching–learning-based optimization algorithm for continuous optimization problems

2021 ◽  
Author(s):  
Po-Chou Shih ◽  
Yang Zhang ◽  
Xizhao Zhou
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Monitor System ◽  
Teaching Learning Based Optimization ◽  
Gaussian Perturbation ◽  
Continuous Optimization Problems ◽  
Teaching Learning

scholarly journals A New Optimization Algorithm Based on Search and Rescue Operations

2019 ◽  
Vol 2019 ◽  
pp. 1-23 ◽  
Author(s):  
Amir Shabani ◽  
Behrouz Asgarian ◽  
Saeed Asil Gharebaghi ◽  
Miguel A. Salido ◽  
Adriana Giret
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Search And Rescue ◽  
Rank Test ◽  
Engineering Design Problems ◽  
Continuous Optimization Problems

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

An Improved Ant Colony Optimization Algorithm Based on Immunization Strategy

2012 ◽  
Vol 490-495 ◽  
pp. 66-70
Author(s):  
Yang Nan
Keyword(s):  
Evolutionary Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Artificial Immune ◽  
Combination Optimization ◽  
Continuous Optimization Problems

Ant colony optimization has been become a very useful method for combination optimization problems. Based on close connections between combination optimization and continuous optimization, nowadays some scholars have studied to apply ant colony optimization to continuous optimization problems, and proposed some continuous ant colony optimizations. To improve the performance of those continuous ant colony optimizations, here the principles of evolutionary algorithm and artificial immune algorithm have been combined with the typical continuous Ant Colony Optimization, and the adaptive Cauchi mutation and thickness selection are used to operate the ant individual, so a new Immunized Ant Colony Optimization is proposed.

Multiobjective optimization of in situ process parameters in preparation of Al-4.5%Cu–TiC MMC using a grey relation based teaching–learning-based optimization algorithm

2017 ◽  
Vol 232 (4) ◽  
pp. 393-407 ◽  
Author(s):  
Biswajit Das ◽  
Susmita Roy ◽  
RN Rai ◽  
SC Saha
Keyword(s):  
Objective Function ◽  
Metal Matrix Composite ◽  
Parameter Optimization ◽  
Process Parameters ◽  
Optimization Algorithm ◽  
Metal Matrix ◽  
Optimization Problems ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In modern in situ composite fabrication processes, the selection of optimal process parameters is greatly important for the preparation of best quality metal matrix composite. For achieving high-quality composite, an efficient optimization technique is essential. The present study explores the potential of a new robust algorithm named teaching–learning-based optimization algorithm for in situ process parameter optimization problems in fabrication of Al-4.5%Cu–TiC metal matrix composite fabricated by stir casting technique. Optimization process is carried out for optimizing the in situ processing parameters i.e. pouring temperature, stirring speed, reaction time for achieving better mechanical properties, i.e. better microhardness, toughness, and ultimate tensile strength. Taguchi’s L25 orthogonal array design of experiment was used for performing the experiments. Grey relational analysis is used for the conversion of the multiobjective function into a single objective function, which is being used as the objective function in the teaching–learning-based optimization algorithm. Confirmation test results show that the developed teaching–learning-based optimization model is a very efficient and robust approach for engineering materials process parameter optimization problems.

Solving chiller loading optimization problems using an improved teaching-learning-based optimization algorithm

2017 ◽  
Vol 39 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Pei-yong Duan ◽  
Jun-qing Li ◽  
Yong Wang ◽  
Hong-yan Sang ◽  
Bao-xian Jia
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems

Sadhana ◽  
2021 ◽  
Vol 46 (2) ◽  
Author(s):  
İbrahim Berkan Aydilek ◽  
İzzettin Hakan Karaçizmeli ◽  
Mehmet Emin Tenekeci ◽  
Serkan Kaya ◽  
Abdülkadir Gümüşçü
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Swarm Optimization ◽  
Continuous Optimization Problems
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