A Tunneling Based Method for Mixed-Discrete Constrained Nonlinear Optimization

1998 ◽  
Author(s):  
Gregory J. Kott ◽  
Gary A. Gabriele
Keyword(s):  
Optimization Problems ◽  
Heuristic Method ◽  
New Method ◽  
Second Phase ◽  
Two Phase ◽  
Design Problems ◽  
Classical Design ◽  
Continuous Problems ◽  
Discrete Optimization Problems ◽  
Better Than

Abstract This paper describes the development of a new method to solve mixed-discrete optimization problems. The method is a two phase approach similar to Tunnel Methods developed for global optimization of continuous problems. It uses a SQP optimization solver in the first phase and an efficient rounding procedure to find discrete solutions in the second phase. All components utilized in this heuristic method are implemented with an emphasis on efficiency. The method was implemented in MATLAB and the solutions of three classical design problems are given. The results show the new method is very robust in finding high quality solutions which are consistently as good or better than past published results.

scholarly journals An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

2022 ◽  
Vol 19 (1) ◽  
pp. 473-512
Author(s):  
Rong Zheng ◽  
◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Population Diversity ◽  
Test Functions ◽  
Design Problems ◽  
Local Optima ◽  
Switching Mechanism ◽  
Engineering Design Problems

<abstract> <p>Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (<italic>RMOP</italic>) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.</p> </abstract>

scholarly journals AN INTRODUCTION OF KRILL HERD ALGORITHM FOR ENGINEERING OPTIMIZATION

2015 ◽  
Vol 22 (3) ◽  
pp. 302-310 ◽  
Author(s):  
Amir H. GANDOMI ◽  
Amir H. ALAVI
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
State Of The Art ◽  
Engineering Optimization ◽  
Design Problems ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Metaheuristic Optimization Algorithm ◽  
Engineering Optimization Problems ◽  
Better Than

A new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is com­pared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods.

Solution of Travelling Salesman Problem Using Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1206-1212
Author(s):  
Sevda Dayıoğlu Gülcü ◽  
Şaban Gülcü ◽  
Humar Kahramanli
Keyword(s):  
Discrete Optimization ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Heuristic Method ◽  
Ant Colony ◽  
Ant Colony System ◽  
Travelling Salesman ◽  
Ant Colonies ◽  
Discrete Optimization Problems

Recently some studies have been revealed by inspiring from animals which live as colonies in the nature. Ant Colony System is one of these studies. This system is a meta-heuristic method which has been developed based upon food searching characteristics of the ant colonies. Ant Colony System is applied in a lot of discrete optimization problems such as travelling salesman problem. In this study solving the travelling salesman problem using ant colony system is aimed.

scholarly journals An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Deyu Tang ◽  
Jie Zhao ◽  
Jin Yang ◽  
Zhen Liu ◽  
Yongming Cai
Keyword(s):  
Local Search ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Evolutionary Strategy ◽  
Quantum Evolution ◽  
Potential Wells ◽  
Shuffled Frog Leaping ◽  
Evolutionary Operator ◽  
Better Than

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well-known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms.

An Artificial Evolutionary Approach for Solving the Nonlinear Constrained Optimization Problems

2013 ◽  
Vol 479-480 ◽  
pp. 861-864
Author(s):  
Yi Chih Hsieh ◽  
Peng Sheng You
Keyword(s):  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Evolutionary Approach ◽  
Benchmark Problems ◽  
Second Phase ◽  
Constrained Optimization Problem ◽  
Constrained Optimization Problems ◽  
Two Phase ◽  
Nonlinear Constrained Optimization

In this paper, an artificial evolutionary based two-phase approach is proposed for solving the nonlinear constrained optimization problems. In the first phase, an immune based algorithm is applied to solve the nonlinear constrained optimization problem approximately. In the second phase, we present a procedure to improve the solutions obtained by the first phase. Numerical results of two benchmark problems are reported and compared. As shown, the solutions by the new proposed approach are all superior to those best solutions by typical approaches in the literature.

Indonesian News Classification Using Naïve Bayes and Two-Phase Feature Selection Model

2017 ◽  
Vol 8 (3) ◽  
pp. 610 ◽  
Author(s):  
M. Ali Fauzi ◽  
Agus Zainal Arifin ◽  
Sonny Christiano Gosaria
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Information Gain ◽  
Information Filtering ◽  
Feature Selection Method ◽  
Feature Space ◽  
Online News ◽  
New Method ◽  
Second Phase ◽  
Two Phase

Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.

scholarly journals Application Analysis on PSO Algorithm in the Discrete Optimization Problems

2021 ◽  
Vol 2078 (1) ◽  
pp. 012018
Author(s):  
Qinglong Chen ◽  
Yong Peng ◽  
Miao Zhang ◽  
Quanjun Yin
Keyword(s):  
Discrete Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Pso Algorithm ◽  
Discrete Optimization Problem ◽  
Spatial Search ◽  
Continuous Problems ◽  
Application Analysis ◽  
Discrete Pso ◽  
Discrete Optimization Problems

Abstract Particle Swarm Optimization (PSO) is kind of algorithm that can be used to solve optimization problems. In practice, many optimization problems are discrete but PSO algorithm was initially designed to meet the requirements of continuous problems. A lot of researches had made efforts to handle this case and varieties of discrete PSO algorithms were proposed. However, these algorithms just focus on the specific problem, and the performance of it significantly degrades when extending the algorithm to other problems. For now, there is no reasonable unified principle or method for analyzing the application of PSO algorithm in discrete optimization problem, which limits the development of discrete PSO algorithm. To address the challenge, we first give an investigation of PSO algorithm from the perspective of spatial search, then, try to give a novel analysis of the key feature changes when PSO algorithm is applied to discrete optimization, and propose a classification method to summary existing discrete PSO algorithms.

A Heuristic Method for Minimizing Makespan in M-Stage Hybrid Flowshop

2012 ◽  
Author(s):  
Kazuko Morizawa ◽  
Naoki Hirabayashi
Keyword(s):  
Numerical Experiments ◽  
Parallel Machines ◽  
Heuristic Method ◽  
Computation Time ◽  
Second Phase ◽  
Scheduling Problem ◽  
Unrelated Parallel Machines ◽  
Two Phase ◽  
Hybrid Flowshop ◽  
Optimum Schedule

This paper deals with a scheduling problem to minimize makespan in m-stage hybrid flowshop with unrelated parallel-machines at least one stage. Since the problem is known to be NP-hard, a two-phase heuristic algorithm is proposed to obtain a near-optimum schedule efficiently. In the first phase of the proposed algorithm, some promising schedules with an identical job-sequence to all stages are generated by applying NEH algorithm in various ways, and then search better schedules by applying some heuristic job-moving strategies to these schedules in the second phase. Numerical experiments are implemented to demonstrate that the proposed method can provide a near-optimum schedule within a reasonable computation time.

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