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 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.

A HYBRID ARTIFICIAL BEE COLONY OPTIMIZATION AND QUANTUM EVOLUTIONARY ALGORITHM FOR CONTINUOUS OPTIMIZATION PROBLEMS

2010 ◽  
Vol 20 (01) ◽  
pp. 39-50 ◽  
Author(s):  
HAI-BIN DUAN ◽  
CHUN-FANG XU ◽  
ZHI-HUI XING
Keyword(s):  
Evolutionary Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Experimental Comparison ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Comparison Results ◽  
Bee Colony Optimization ◽  
Continuous Optimization Problems

In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.

scholarly journals Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

2021 ◽  
Vol 11 (14) ◽  
pp. 6449
Author(s):  
Fernando Peres ◽  
Mauro Castelli
Keyword(s):  
Combinatorial Optimization ◽  
Scientific Community ◽  
Optimization Problems ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Combinatorial Optimization Problems ◽  
The Past ◽  
Definition Of ◽  
Problem Solvers ◽  
New Algorithms

In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.

scholarly journals APPLICATION OF ANT OPTIMIZATION ALGORITHMS IN THE SOLUTION OF THE ROUTING PROBLEM

2019 ◽  
Vol 6 (58) ◽  
pp. 75-83
Author(s):  
E. Skakalina
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Heuristic Method ◽  
Computer Experiments ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Artificial Ants ◽  
Software Product ◽  
Subsequent Section

The article discusses current issues of using evolutionary algorithms to solve the routing problem. Ant algorithms (MA), like most types of evolutionary algorithms, are based on the use of a population of potential solutions and are designed to solve combinatorial optimization problems, first of all, search for various paths on graphs. The cooperation between individuals (artificial ants) is implemented on the basis of stigmetry modeling. In addition, each agent, called artificial ant, is looking for a solution to the problem. Artificial ants consistently build a solution to the problem, moving around the graph, lay the pheromone and, when choosing a further section of the path, take into account the concentration of this enzyme. The higher the concentration of pheromone in the subsequent section, the greater the likelihood of its choice. Since MA is based on the movement of ants along some paths, MAs are effective, first of all, in solving problems that can be interpreted in the form of a graph. Computer experiments showed that the efficiency of MA increases with increasing dimension of the problem and for tasks on high-dimensional graphs they work faster than other evolutionary algorithms. Good results were also noted in solving non-stationary problems on graphs with a changing environment. In connection with this, the implementation of the meta - heuristic method is proposed as a modification of ant optimization algorithms. The scheme of the system is presented. A software product specification is also provided

Designing Parallel Meta-Heuristic Methods

2014 ◽  
pp. 260-280 ◽  
Author(s):  
Teodor Gabriel Crainic ◽  
Tatjana Davidović ◽  
Dušan Ramljak
Keyword(s):  
High Performance ◽  
Optimization Problems ◽  
Real Life ◽  
Neighborhood Search ◽  
Heuristic Methods ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
And Control ◽  
Reasonable Computing

Meta-heuristics represent powerful tools for addressing hard combinatorial optimization problems. However, real life instances usually cannot be treated efficiently in “reasonable” computing times. Moreover, a major issue in meta-heuristic design and calibration is to provide high performance solutions for a variety of problems. Parallel meta-heuristics aim to address both issues. The objective of this chapter is to present a state-of-the-art survey of the main parallelization ideas and strategies, and to discuss general design principles applicable to all meta-heuristic classes. To achieve this goal, the authors explain various paradigms related to parallel meta-heuristic development, where communications, synchronization, and control aspects are the most relevant. They also discuss implementation issues pointing out the characteristics of shared and distributed memory multiprocessors as target architectures. All these topics are illustrated by the examples from recent literature related to the parallelization of various meta-heuristic methods. Here, the authors focus on Variable Neighborhood Search and Bee Colony Optimization.

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.

scholarly journals Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
E. Osaba ◽  
R. Carballedo ◽  
F. Diaz ◽  
E. Onieva ◽  
I. de la Iglesia ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Comparative Analysis ◽  
Combinatorial Optimization ◽  
Scientific Community ◽  
Statistical Study ◽  
Optimization Problems ◽  
Basic Structure ◽  
Evolutionary Strategy ◽  
Mutation Process ◽  
Combinatorial Optimization Problems

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distributionz-test.

scholarly journals Analysis of the Results of Applying the Bee Colony Method in the Problem of Coloring General Graphs

2021 ◽  
Vol 24 (4) ◽  
pp. 126-145
Author(s):  
А. O. Pshenichnykh ◽  
E. I. Vatutin
Keyword(s):  
Convergence Rate ◽  
Chromatic Number ◽  
Optimization Problems ◽  
Random Search ◽  
Heuristic Method ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Wide Range

Purpose of research. We have discovered a wide range of problems that are important in practice and which can be reduced in polynomial time to discrete combinatorial optimization problems, many of which can be solved using graph theory. One of these tasks is finding the chromatic number of a graph and its corresponding coloring. Taking into account the fact that the combinatorial problem of finding the chromatic number of a graph belongs to the complexity class and does not allow obtaining an optimal solution in a rational time for problems of practically important dimension, the search for a suitable heuristic method that allows obtaining high-quality solutions with low costs required for computation is demanded and relevant. The aim of the study is to analyze the results of using the bee colony method in the task at hand. The tasks of this research are: description of algorithmic techniques in a formalized form, which make it possible to apply the bee colony method in the problem to be solved, making modifications to the bee colony method that increase the efficiency of the method, namely the quality of the resulting final colorings, as well as the determination of factors affecting the quality and the time spent in finding solutions. Methods. To conduct research in the selected area, computational experiments were organized based on the use of heuristic methods in the problem under consideration. Meta-optimization of the tuning parameters of the methods and determination of their convergence rate was carried out, as well as a comparison of the quality and time of obtaining solutions. Results. As a result of the study, the convergence rate of the method was found to be higher than that of the random walk method; the dependence of the quality of the resulting final colorings on the graph size N and density d was found. It was found that the chosen method is faster than the method of weighted random enumeration with the variation of vertices according to the minimum of admissible colors on »67% , which currently generates solutions with the lowest chromatic number, while losing quality to it on »7% . A higher rate of convergence was noticed when compared with the method of random walks, the principle of which is the same as that of foraging bees. Conclusion. It was found that the bee colony method finds colorings with the same average chromatic number in fewer iterations than the random walk method, i.e. it has a higher convergence rate, while remaining significantly fast relative to the method of random search with a variation of vertices to reduce the allowed colors.

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