scholarly journals Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

2021 ◽  
Author(s):  
Ali Ebrahimnejad ◽  
Mohammad Enayattabr ◽  
Homayun Motameni ◽  
Harish Garg
Keyword(s):  
Shortest Path ◽  
Artificial Bee Colony ◽  
Fuzzy Numbers ◽  
Pso Algorithm ◽  
Convergence Time ◽  
Bee Colony ◽  
Different Types ◽  
Implementation Time ◽  
Fuzzy Network ◽  
Interval Valued

AbstractIn recent years, numerous researchers examined and analyzed several different types of uncertainty in shortest path (SP) problems. However, those SP problems in which the costs of arcs are expressed in terms of mixed interval-valued fuzzy numbers are less addressed. Here, for solving such uncertain SP problems, first a new procedure is extended to approximate the summation of mixed interval-valued fuzzy numbers using alpha cuts. Then, an extended distance function is introduced for comparing the path weights. Finally, we intend to use a modified artificial bee colony (MABC) algorithm to find the interval-valued membership function of SP in such mixed interval-valued fuzzy network. The proposed algorithm is illustrated via two applications of SP problems in wireless sensor networks and then the results are compared with those derived from genetic and particle swarm optimization (PSO) algorithms, based on three indexes convergence iteration, convergence time and run time. The obtained results confirm that the MABC algorithm has less convergence iteration, convergence time and implementation time compared to GA and PSO algorithm.

scholarly journals Mehar Approach for Finding Shortest Path in Supply Chain Network

2021 ◽  
Vol 13 (7) ◽  
pp. 4016
Author(s):  
Tanveen Kaur Bhatia ◽  
Amit Kumar ◽  
Srimantoorao S. Appadoo ◽  
Yuvraj Gajpal ◽  
Mahesh Kumar Sharma
Keyword(s):  
Shortest Path ◽  
Fuzzy Numbers ◽  
New Approach ◽  
Shortest Path Problems ◽  
Shortest Distance ◽  
Lexicographic Method ◽  
Different Types ◽  
The Cost ◽  
Pythagorean Fuzzy Numbers ◽  
Interval Valued

The aim of each company/industry is to provide a final product to customers at the minimum possible cost, as well as to protect the environment from degradation. Ensuring the shortest travel distance between involved locations plays an important role in achieving the company’s/industry’s objective as (i) the cost of a final product can be minimized by minimizing the total distance travelled (ii) finding the shortest distance between involved locations will require less fuel than the longest distance between involved locations. This will eventually result in lesser degradation of the environment. Hence, in the last few years, various algorithms have been proposed to solve different types of shortest path problems. A recently proposed algorithm for solving interval-valued Pythagorean fuzzy shortest path problems requires excessive computational efforts. Hence, to reduce the computational efforts, in this paper, firstly, an alternative lexicographic method is proposed for comparing interval-valued Pythagorean fuzzy numbers. Then, using the proposed lexicographic comparing method, a new approach (named as Mehar approach) is proposed to solve interval-valued Pythagorean fuzzy shortest path problems. Furthermore, the superiority of the proposed lexicographic comparing method, as well as the proposed Mehar approach, is discussed.

scholarly journals Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 603 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Cheng-Chieh Hsieh
Keyword(s):  
Particle Swarm Optimization ◽  
Artificial Bee Colony ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Global Maximum ◽  
Photovoltaic Module ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Learning Factor

In this study, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking performance of the MPP tracker. Finally, MATLAB software was used to carry out a simulation and prove the improved that the PSO algorithm successfully tracked the MPP in the photovoltaic array output curve with multiple peaks. Its tracking performance is far superior to the existing PSO algorithm.

scholarly journals Power Loss Minimization in Radial Distribution Systems with Simultaneous Placement and Sizing of Different Types of Distribution Generation Units Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 12 (3) ◽  
pp. 686-707
Author(s):  
Pavan Khetrapal ◽  
◽  
Jafarullakhan Pathan ◽  
Shivam Shrivastava ◽  
◽  
...  
Keyword(s):  
Power Loss ◽  
Artificial Bee Colony Algorithm ◽  
Distribution Systems ◽  
Artificial Bee Colony ◽  
Economic Incentives ◽  
The Other ◽  
Loss Minimization ◽  
Bee Colony ◽  
Different Types ◽  
Distribution Generation

Poweraloss is considered as one of theaimportant indicators used toaquantify theaperformance of distributionsnetworks. Minimisation of power lossesawith integration of distributedsgeneration (DG) unitssin distributionisystems has gained significantimomentum due to the associateditechno – economic incentives. Inithis paper, a noveliImproved Artificial Bee ColonyiAlgorithm (IABC) is developed toirobustly detect the optimalisite and sizedof DG units for minimisation of total poweralosses without violatingathe equality anddinequality constraints. Theiproposed algorithm is simulated in MATLABienvironment, and theieffectiveness of theialgorithm is validatedron IEEE – 34 bus andtIEEE – 69 bus radialrdistribution systems. Therperformance of thetproposed techniquerhas been validated by comparingsthe results obtained fromsother competesalgorithms. Comparisons showithat the proposed technique is moreiefficient in terms of simulationiresults of power loss andiconvergence propertyithan the other reportedialgorithms, suggesting that theisolution obtained is a globalioptimum.

An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 561-600
Author(s):  
Selcuk Aslan
Keyword(s):  
Big Data ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Abc Algorithm ◽  
Bee Colony ◽  
De Algorithm ◽  
Data Optimization

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

Characteristic Analysis of Artificial Bee Colony Algorithm with Network-Structure

2017 ◽  
Vol 21 (3) ◽  
pp. 496-506
Author(s):  
Shunta Imamura ◽  
◽  
Toshiya Kaihara ◽  
Nobutada Fujii ◽  
Daisuke Kokuryo ◽  
...  
Keyword(s):  
Network Structure ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Computer Experiments ◽  
High Dimensional ◽  
Characteristic Analysis ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Different Types ◽  
Efficient Exploration

The artificial bee colony (ABC) algorithm, which is inspired by the foraging behavior of honey bees, is one of the swarm intelligence systems. This algorithm can provide an efficient exploration of the optimal solutions using three different types of agents for optimization problems with multimodal functions. However, the performance of the conventional ABC algorithm decreases for high-dimensional problems. In this study, we propose an improved algorithm using the network structure of agents to enhance the ability for global search. The efficacy of the proposed algorithm is evaluated by performing computer experiments with high-dimensional benchmark functions.

scholarly journals Optimal Management of Reactive Power Considering Voltage and Location of Control Devices Using Artificial Bee Algorithm

2021 ◽  
Vol 12 (1) ◽  
pp. 27
Author(s):  
Hassan Shokouhandeh ◽  
Sohaib Latif ◽  
Sadaf Irshad ◽  
Mehrdad Ahmadi Kamarposhti ◽  
Ilhami Colak ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Optimization Problems ◽  
Pso Algorithm ◽  
Reactive Power Compensation ◽  
Voltage Profile ◽  
Bee Colony ◽  
Control Devices

Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

scholarly journals Shortest Path Selection Algorithm for Cold Chain Logistics Transportation Based on Improved Artificial Bee Colony

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhihuan Liu
Keyword(s):  
Shortest Path ◽  
Artificial Bee Colony Algorithm ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Selection Effect ◽  
Path Selection ◽  
Cold Chain ◽  
Selection Algorithm ◽  
Selection Accuracy ◽  
Bee Colony

Aiming at the problems of low shortest path selection accuracy, longer response time, and poor selection effect in current cold chain logistics transportation methods, a cold chain logistics transportation shortest path selection algorithm based on improved artificial bee colony is proposed. The improved algorithm is used to initialize the food source, reevaluate the fitness value of the food source, generate a new food source, optimize the objective function and food source evaluation strategy, and get an improved artificial bee colony algorithm. Based on the improved artificial bee colony algorithm, the group adaptive mechanism of particle swarm algorithm is introduced to initialize the position and velocity of each particle randomly. Dynamic detection factor and octree algorithm are adopted to dynamically update the path of modeling environment information. According to the information sharing mechanism between individual particles, the group adaptive behavior control is performed. After the maximum number of cycles, the path planning is completed, the shortest path is output, and the shortest path selection of cold chain logistics transportation is realized. The experimental results show that the shortest path selection effect of the cold chain logistics transportation of the proposed algorithm is better, which can effectively improve the shortest path selection accuracy and reduce the shortest path selection time.

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

Measurement ◽  
2016 ◽  
Vol 93 ◽  
pp. 48-56 ◽  
Author(s):  
Ali Ebrahimnejad ◽  
Madjid Tavana ◽  
Hamidreza Alrezaamiri
Keyword(s):  
Shortest Path ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Shortest Path Problems ◽  
Bee Colony
Sign in / Sign up

Export Citation Format

Share Document