Solving Traveling Salesman Problem by Using Combinatorial Artificial Bee Colony Algorithms

2019 ◽  
Vol 28 (01) ◽  
pp. 1950004 ◽  
Author(s):  
Dervis Karaboga ◽  
Beyza Gorkemli
Keyword(s):  
Artificial Bee Colony ◽  
Standard Form ◽  
Optimization Methods ◽  
Traveling Salesman ◽  
The Other ◽  
Optimization Approach ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Convergence Performance ◽  
Simulation Results

Artificial bee colony (ABC) is a quite popular optimization approach that has been used in many fields, with its not only standard form but also improved versions. In this paper, new versions of ABC algorithm to solve TSP are introduced and described in detail. One of these is the combinatorial version of standard ABC, called combinatorial ABC (CABC) algorithm. The other one is an improved version of CABC algorithm, called quick CABC (qCABC) algorithm. In order to see the efficiency of the new versions, 15 different TSP benchmarks are considered and the results generated are compared with different well-known optimization methods. The simulation results show that, both CABC and qCABC algorithms demonstrate good performance for TSP and also the new definition in quick ABC (qABC) improves the convergence performance of CABC on TSP.

Artificial Bee Colony Algorithm for Traveling Salesman Problem

2011 ◽  
Vol 314-316 ◽  
pp. 2191-2196 ◽  
Author(s):  
Wei Hua Li ◽  
Wei Jia Li ◽  
Yuan Yang ◽  
Hai Qiang Liao ◽  
Ji Long Li ◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Nearest Neighbor ◽  
Traveling Salesman ◽  
Comparison Study ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Benchmark Instances ◽  
Bee Colony Optimization

By combining the modified nearest neighbor approach and the improved inver-over operation, an Artificial Bee Colony (ABC) Algorithm for Traveling Salesman Problem (TSP) is proposed in this paper. The heuristic approach was tested in some benchmark instances selected from TSPLIB. In addition, a comparison study between the proposed algorithm and the Bee Colony Optimization (BCO) model is presented. Experimental results show that the presented algorithm outperforms the BCO method and can efficiently tackle the small and medium scale TSP instances.

scholarly journals An Improved ABC Algorithm and Its Application in Bearing Fault Diagnosis with EEMD

Algorithms ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 72 ◽  
Author(s):  
Weijia Chen ◽  
Yancai Xiao
Keyword(s):  
Fault Diagnosis ◽  
Artificial Bee Colony ◽  
The Other ◽  
Abc Algorithm ◽  
Original Algorithm ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Selection Of ◽  
Improved Algorithm

The Ensemble Empirical Mode Decomposition (EEMD) algorithm has been used in bearing fault diagnosis. In order to overcome the blindness in the selection of white noise amplitude coefficient e in EEMD, an improved artificial bee colony algorithm (IABC) is proposed to obtain it adaptively, which providing a new idea for the selection of EEMD parameters. In the improved algorithm, chaos initialization is introduced in the artificial bee colony (ABC) algorithm to insure the diversity of the population and the ergodicity of the population search process. On the other hand, the collecting bees are divided into two parts in the improved algorithm, one part collects the optimal information of the region according to the original algorithm, the other does Levy flight around the current global best solution to improve its global search capabilities. Four standard test functions are used to show the superiority of the proposed method. The application of the IABC and EEMD algorithm in bearing fault diagnosis proves its effectiveness.

scholarly journals DECREASING ACTUAL POWER LOSS BY REFINED ABC ALGORITHM

2017 ◽  
Vol 5 (10) ◽  
pp. 63-71
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Real Power ◽  
Abc Algorithm ◽  
The Real ◽  
Bee Colony ◽  
Search Speed ◽  
Convergence Performance ◽  
Power Problem

Refined ABC algorithm (RABC) proposed in this paper to solve the optimal reactive power problem. An artificial bee colony (ABC) algorithm is one of copious swarm intelligence algorithms that employ the foraging behavior of honeybee colonies. To progress the convergence performance and search speed of finding the best solution RABC algorithm has been developed. The main objective in this problem is to minimize the real power loss and also to keep the variables within the specified limits. Proposed Refined ABC (RABC) algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulations results reveal about the better performance of the proposed Refined ABC algorithm (RABC) algorithm in reducing the real power loss and the voltage profiles within the limits.

scholarly journals A New Emigrant Creation Strategy with Randomized Sources for Parallel Artificial Bee Colony Algorithm

2018 ◽  
pp. 81-86
Author(s):  
Selcuk Aslan ◽  
Dervis Karaboga ◽  
Alperen Aksoy
Keyword(s):  
Artificial Bee Colony ◽  
Parallel Implementation ◽  
Experimental Studies ◽  
Population Diversity ◽  
Computational Power ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Convergence Performance ◽  
Neighborhood Topology ◽  
Current Systems

Dividing the whole population into subpopulations or subcolonies then evaluating them simultaneously is one of the most commonly used parallelization approaches to utilize the computational power of the current systems. However, this type of parallelization strategy decreases the population diversity because of the division of the entire population and needs migrations between subpopulations to maintain the solution diversity until the end of the iterations. In this study, we proposed a new emigrant creation strategy in which the parameters of the best food source being migrated to the neighbor subpopulation is modified with the more appropriate parameters of the randomly determined solution or solutions and investigated its effect on the performance of the parallel Artificial Bee Colony (ABC) algorithm. Experimental studies showed that newly proposed emigrant creation strategy based on randomized solutions significantly improved the convergence performance and solution qualities of parallel ABC algorithm compared to the its standard serial and ring neighborhood topology based parallel implementation for which the best solutions are directly used as emigrants.

Design and Development of a Modified Artificial Bee Colony Approach

2020 ◽  
pp. 365-381
Author(s):  
Saad T. Alharbi
Keyword(s):  
Local Search ◽  
Real World ◽  
Artificial Bee Colony ◽  
Search Algorithms ◽  
Traveling Salesman ◽  
Benchmark Problem ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Benchmark Instances ◽  
The Traveling Salesman Problem

The traveling thief problem (TTP) is a benchmark problem that consists of two well-known problems, the traveling salesman problem (TSP) and the knapsack problem (KP). It was defined to imitate complex real-world applications that comprise different interdependent sub-problems. Various approaches were proposed in the literature to solve such a problem. These approaches mostly focus on local search algorithms, heuristics methods and evolutionary approaches. In addition, some of these approaches concentrated on solving the problem by considering each sub-problem independently. Thus far, limited approaches were proposed to solve the problem using swarm intelligence. In this article, the authors introduce a modified artificial bees colony (ABC) algorithm that addresses the TTP in an interdependent manner. The performance of this approach was compared with various recent approaches in the literature using different benchmark instances. The obtained results demonstrated that it is competitive with the state-of-the-art approaches, especially on small and medium instances.

A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm

2016 ◽  
Vol 15 (02) ◽  
pp. 1650011 ◽  
Author(s):  
Wang Yong ◽  
Wang Tao ◽  
Zhang Cheng-Zhi ◽  
Huang Hua-Juan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Bat Algorithm ◽  
Global Optimum ◽  
The Other ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Fast Convergence Rate

A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and artificial bee colony (ABC), in the terms of the ability to find the global optimum of a range of the popular benchmark functions. The experimental results show that the proposed optimization seems superior to the other three algorithms, and the proposed algorithm has the performance of fast convergence rate, and high local optimal avoidance.

scholarly journals Bičių spiečių imitavimas sprendžiant optimizavimo uždavinius

2011 ◽  
Vol 56 ◽  
pp. 163-173
Author(s):  
Alfonsas Misevičius ◽  
Jonas Blonskis ◽  
Vytautas Bukšnaitis
Keyword(s):  
Swarm Intelligence ◽  
Honey Bees ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Optimization Methods ◽  
Quadratic Assignment ◽  
Optimization Approach ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Straipsnyje nagrinėjami klausimai, susiję su naujoviškų metodų taikymu sprendžiant optimizavimo uždavinius. Šiuo konkrečiu atveju diskutuojama apie bičių spiečių elgsenos imitavimą ir galimą jo taikymą kombinatorinio (diskretinio) tipo optimizavimo uždaviniams. Straipsnio pradžioje aptariami konceptualūs aspektai ir bendroji bičių spiečių imitavimo algoritmų idėja. Aprašoma bičių spiečiaus imitavimo algoritmo realizacija atskiram nagrinėjamam atvejui – kvadratinio paskirstymo uždaviniui, kuris yra vienas iš aktualių ir sudėtingų kombinatorinio optimizavimo uždavinių pavyzdžių. Straipsnyje pateikiami ir su realizuotu algoritmu atliktų eksperimentų rezultatai, kurie iliustruoja skirtingų veiksnių (parametrų) įtaką gaunamų sprendinių kokybei ir patvirtina aukštą algoritmo efektyvumo lygį.Bee Swarm Intelligence in (Combinatorial) OptimizationAlfonsas Misevičius, Jonas Blonskis, Vytautas Bukšnaitis SummaryIn this paper, we discuss some issues related to the innovative intelligent optimization methods. More precisely, we are concerned with the bee colony optimization approach, which is inspired by the behaviour of natural swarms of honey bees. Both the conceptual methodological facets of the swarm intelligence paradigm and the aspects of implementation of the artificial bee colony algorithms are considered. In particular, we introduce an implementation of the artificial bee colony optimization algorithm for the well-known combinatorial optimization problem of quadratic assignment (QAP). The results of computational experiments with different variants of the implemented algorithm are also presented and discussed. Based on the obtained results, it is concluded that the proposed algorithm may compete with other efficient heuristic techniques. 

scholarly journals Modeling of higher order systems using artificial bee colony algorithm

2016 ◽  
Vol 6 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Aytekin Bağış ◽  
Halit Senberber
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
System Modeling ◽  
Higher Order ◽  
Model Parameters ◽  
Powerful Method ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Proposed Model ◽  
Simulation Results

In this work, modeling of the higher order systems based on the use of the artificial bee colony (ABC) algorithm were examined. Proposed model parameters for the sample systems in the literature were obtained by using the algorithm, and its performance was presented comparatively with the other methods. Simulation results show that the ABC algorithm based system modeling approach can be used as an efficient and powerful method for higher order systems.

A quasi-affine transformation artificial bee colony algorithm for global optimization

2021 ◽  
pp. 1-18
Author(s):  
Baohua Zhao ◽  
Tien-Wen Sung ◽  
Xin Zhang
Keyword(s):  
Affine Transformation ◽  
Artificial Bee Colony ◽  
Triangular Matrix ◽  
Convergence Speed ◽  
Test Set ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Search Mechanism ◽  
Multiple Dimensions ◽  
Collaborative Search

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.

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