scholarly journals Continuous optimization and combinatorial optimization

2009 ◽  
Vol 5 (1) ◽  
pp. 1-2
Author(s):  
Liqun Qi ◽  
Li-Zhi Liao ◽  
Wenan Zang ◽  
Guanglu Zhou
Keyword(s):  
Combinatorial Optimization ◽  
Continuous Optimization

scholarly journals Clustering Multiple Hydrographs Using Mathematical Optimization

10.29007/6r61 ◽  
2018 ◽  
Author(s):  
Kazuhiro Matsumoto ◽  
Mamoru Miyamoto
Keyword(s):  
Combinatorial Optimization ◽  
Water Level ◽  
Continuous Optimization ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
The Other ◽  
Flood Events ◽  
Number Of Clusters ◽  
Different Characteristics ◽  
Insight Into

A mathematical optimization procedure is presented to group multiple hydrographs into a small number of clusters for the purpose of helping to understand various runoff behaviors observed in flood events in a basin. In grouping, the hydrographs belonging to each cluster can be estimated within the specified accuracy by the corresponding parameter set. The effectiveness is demonstrated using twenty-seven hydrographs observed in nine flood events and at three water level stations in the Abe River basin in Japan. The optimization results illustrate that eight sets of parameters are necessary to estimate such hydrographs within the specified accuracy. One parameter set commonly estimates as many as seven out of twenty-seven hydrographs while some other parameter sets estimate the other hydrographs with different characteristics specific to flood events or water level stations. Most of the previous research is based on continuous optimization; however, a presenting procedure such as clustering is based on combinatorial optimization. Thus, new insight into understanding the runoff behaviors is brought by combinatorial optimization which is not often used in previous research.

Nonlinear Integer, Discrete and Continuous Optimization Using Adaptive Range Genetic Algorithms

1997 ◽  
Author(s):  
Masao Arakawa ◽  
Ichiro Hagiwara
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Discrete Optimization ◽  
Continuous Optimization ◽  
The Other ◽  
Computational Time ◽  
Test Problems ◽  
Design Variables ◽  
Discrete And Continuous Optimization

Abstract Genetic algorithms are effective algorithms for large scaled combinatorial optimization. They are potentially effective in integer and discrete optimization. However, as they are not well coded to its tedious expression in converting chromosomes to design variables, we need to do some special efforts to overcome these flaws. In the proposed method, it automatically adapts searching ranges according to the situation of the generation. Thus, we are free from these flaws. Moreover, we don’t have to give too many genes to chromosome, we can save computational time and memory and the convergence becomes better. In this paper, we combine the proposed integer and discrete adaptive range genetic algorithms and adaptive real range genetic algorithms which we presented in the previous studies, and present an extended genetic algorithms method. We applied the proposed method to well-known test problems, compare the results with the other methods and show its effectiveness.

scholarly journals Load Balancing Based on Optimization Algorithms: An Overview

2019 ◽  
Vol 4 (2019) ◽  
pp. 3-12
Author(s):  
Fatma Mbarek ◽  
Volodymyr Mosorov
Keyword(s):  
Combinatorial Optimization ◽  
Load Balancing ◽  
Communication Networks ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Real Life ◽  
Continuous Optimization ◽  
Combinatorial Optimization Problems ◽  
Life Problems ◽  
Continuous Optimization Problems

Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issues.

SOLVING A SINGLE MACHINE SCHEDULING PROBLEM BY A DISCRETE VERSION OF ELECTROMAGNETISM-LIKE METHOD

2009 ◽  
Vol 18 (08) ◽  
pp. 1597-1608 ◽  
Author(s):  
NIKBAKHSH JAVADIAN ◽  
MOHSEN GOLALIKHANI ◽  
REZA TAVAKKOLI-MOGHADDAM
Keyword(s):  
Combinatorial Optimization ◽  
Single Machine ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Machine Scheduling ◽  
Population Based ◽  
Single Machine Scheduling ◽  
Discrete Version ◽  
Scheduling Problem ◽  
Combinatorial Optimization Problems

The electromagnetism-like method (EM) is a population based meta-heuristic algorithm utilizing an attraction-repulsion mechanism to move sample points (i.e., our solutions) towards the optimality. In general, the EM has been initially used for solving continuous optimization problems and could not be applied on combinatorial optimization ones. This paper proposes a discrete binary version of the EM for solving combinatorial optimization problems. To show the efficiency of our proposed EM, we solve a single machine scheduling problem and compare our computational results with the solutions reported in the literature. Finally, we conclude that our proposed method is capable of solving such well-known problems more efficiently than the previous studies.

Ant Colony Optimization for Neural Network

2008 ◽  
Vol 392-394 ◽  
pp. 677-681 ◽  
Author(s):  
H. Mei ◽  
Yong Wang
Keyword(s):  
Neural Network ◽  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Nonlinear Function ◽  
Continuous Optimization ◽  
Ant Colony ◽  
New Learning

A new learning way for neural network (NN) in which its weights can be optimized by using the ant colony algorithm is presented in this paper. The learning of neural network belongs to continuous optimization. The ant colony algorithm is initially developed for hard combinatorial optimization. A kind of ant colony optimization (ACO) for continuous optimization, which includes global searching, local searching and definite searching, is developed based on the basic ant colony algorithm. A three-layer neural network, as an example, is trained to express nonlinear function. The efficiency of the new algorithm is examinated. It is found that the new developed method has the merits of both ant colony algorithm and neural network.

Simultaneous task placement and sequence optimization in an inspection robotic cell

Robotica ◽  
2021 ◽  
pp. 1-21
Author(s):  
MohammadHadi FarzanehKaloorazi ◽  
Ilian A. Bonev ◽  
Lionel Birglen
Keyword(s):  
Combinatorial Optimization ◽  
Cycle Time ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Traveling Salesman Problems ◽  
Inspection Process ◽  
Serial Robot ◽  
Unpredictable Environment ◽  
Task Placement ◽  
Robot Configurations

Abstract In this article, we improve the efficiency of a turbine blade inspection robotic workcell. The workcell consists of a stationary camera and a 6-axis serial robot that is holding a blade and presenting different zones of the blade to the camera for inspection. The problem at hand consists of a 6-DOF (degree of freedom) continuous optimization of the camera placement and a discrete combinatorial optimization of the sequence of inspection poses (images). For each image, all robot configurations (up to eight) are taken into consideration. A novel combined approach is introduced, called blind dynamic particle swarm optimization (BD-PSO), to simultaneously obtain the optimal design for both domains. The objective is to minimize the cycle time of the inspection process, while avoiding any collisions. Even though PSO is vastly used in engineering problems, the novelty of our combinatorial optimization method is in its ability to be used efficiently in traveling salesman problems where the distances between the cities are unknown and subject to change. This highly unpredictable environment is the case of the inspection cell where the cycle time between two images will change for different camera placements.

scholarly journals Bee Colony Optimization - part II: The application survey

2015 ◽  
Vol 25 (2) ◽  
pp. 185-219 ◽  
Author(s):  
Dusan Teodorovic ◽  
Milica Selmic ◽  
Tatjana Davidovic
Keyword(s):  
Combinatorial Optimization ◽  
Scientific Community ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Heuristic Method ◽  
Continuous Optimization ◽  
Natural Behavior ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Bee Colony Optimization

Bee Colony Optimization (BCO) is a meta-heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. BCO has been successfully applied to various hard combinatorial optimization problems, mostly in transportation, location and scheduling fields. There are some applications in the continuous optimization field that have appeared recently. The main purpose of this paper is to introduce the scientific community more closely with BCO by summarizing its existing successful applications.

scholarly journals Elephant Herding Optimization: Variants, Hybrids, and Applications

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1415 ◽  
Author(s):  
Juan Li ◽  
Hong Lei ◽  
Amir H. Alavi ◽  
Gai-Ge Wang
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Algorithm ◽  
State Of The Art ◽  
Continuous Optimization ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Herding Behavior ◽  
Future Directions ◽  
Comprehensive Review ◽  
Metaheuristic Optimization Algorithm

Elephant herding optimization (EHO) is a nature-inspired metaheuristic optimization algorithm based on the herding behavior of elephants. EHO uses a clan operator to update the distance of the elephants in each clan with respect to the position of a matriarch elephant. The superiority of the EHO method to several state-of-the-art metaheuristic algorithms has been demonstrated for many benchmark problems and in various application areas. A comprehensive review for the EHO-based algorithms and their applications are presented in this paper. Various aspects of the EHO variants for continuous optimization, combinatorial optimization, constrained optimization, and multi-objective optimization are reviewed. Future directions for research in the area of EHO are further discussed.

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