scholarly journals An Artificial Bee Colony Algorithm with Random Location Updating

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lijun Sun ◽  
Tianfei Chen ◽  
Qiuwen Zhang
Keyword(s):  
Information Exchange ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Selection Strategy ◽  
Initial Population ◽  
Search Range ◽  
Bee Colony ◽  
Random Location ◽  
Location Updating

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension.

scholarly journals A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Mao ◽  
Heng-you Lan ◽  
Hao-ru Li
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Population Diversity ◽  
Initial Population ◽  
Roulette Wheel Selection ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Random Step ◽  
Poor Population

As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improvedS-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensionsD=20andD=40are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions.

An Artificial Bee Colony Algorithm for Multiobjective Redundancy Allocation Problem in Repairable System

2014 ◽  
Vol 519-520 ◽  
pp. 1401-1404 ◽  
Author(s):  
Qi Fang He ◽  
Zu Tong Wang ◽  
Ming Fa Zheng ◽  
Long Yan
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Multiple Objectives ◽  
Allocation Problem ◽  
Selection Strategy ◽  
Repairable System ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Bee Colony ◽  
Tournament Selection

This paper proposed a multiobjective artificial bee colony algorithm (MOABC) to solve the reliability redundancy allocation problem (RAP) with multiple objectives, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. The number of redundancy components is to be decided so as to maximize the availability and minimize the designing cost of the system simultaneously. It shows that the proposed algorithm can solve multiobjective redundancy allocation problem efficiently.

scholarly journals Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Bai Li
Keyword(s):  
Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Gold Price ◽  
Selection Strategy ◽  
Price Forecasting ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Previous Cycle ◽  
Subsequent Cycle

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.

Population Diversity Guided Dimension Perturbation for Artificial Bee Colony Algorithm

2021 ◽  
pp. 473-485
Author(s):  
Tao Zeng ◽  
Tingyu Ye ◽  
Luqi Zhang ◽  
Minyang Xu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Bee Colony

scholarly journals A Modified Artificial Bee Colony Algorithm Based on Search Space Division and Disruptive Selection Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Zhen-an He ◽  
Caiwen Ma ◽  
Xianhong Wang ◽  
Lei Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Search Space ◽  
Poor Quality ◽  
Population Diversity ◽  
Disruptive Selection ◽  
Selection Strategy ◽  
Abc Algorithm ◽  
Space Division ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has attracted much attention and has been applied to many scientific and engineering applications in recent years. However, there are still some insufficiencies in ABC algorithm such as poor quality of initial solution, slow convergence, premature, and low precision, which hamper the further development and application of ABC. In order to further improve the performance of ABC, we first proposed a novel initialization method called search space division (SSD), which provided high quality of initial solutions. And then, a disruptive selection strategy was used to improve population diversity. Moreover, in order to accelerate convergence rate, we changed the definition of the scout bee phase. In addition, we designed two types of experiments to testify our proposed algorithm. On the one hand, we conducted experiments to make sure how much each modification makes contribution to improving the performance of ABC. On the other hand, comprehensive experiments were performed to prove the superiority of our proposed algorithm. The experimental results indicate that SDABC significantly outperforms other ABCs, contributing to higher solution accuracy, faster convergence speed, and stronger algorithm stability.

Hybrid Artificial Bee Colony Algorithm with Differential Evolution and Free Search for Numerical Function Optimization

2016 ◽  
Vol 25 (04) ◽  
pp. 1650020 ◽  
Author(s):  
Lian Lian ◽  
Fu Zaifeng ◽  
Yang Guangfei ◽  
Huang Yi
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Robustness Analysis ◽  
Selection Strategy ◽  
Local Optimum ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Free Search

Artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

scholarly journals Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wang Chun-Feng ◽  
Liu Kui ◽  
Shen Pei-Ping
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Population Based ◽  
Initial Population ◽  
Bee Colony ◽  
Search Mechanism ◽  
Point Set ◽  
Searching Ability ◽  
Good Point Set

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.

scholarly journals Application study of UAV path planning based on the balanced search factor artificial bee colony algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032064
Author(s):  
Wenlong Hao ◽  
Bo Luo ◽  
Zhiyuan Zhang
Keyword(s):  
Path Planning ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategy ◽  
Local Development ◽  
Population Diversity ◽  
Application Study ◽  
Slow Convergence ◽  
Bee Colony ◽  
Average Accuracy

Abstract In this paper, aiming at the shortcomings of slow convergence speed and weak local search ability of traditional artificial bee colony algorithm in path planning, an artificial bee colony algorithm based on balanced search factor is proposed for UAV path planning. Using a search strategy based on balanced search factor, the depth search is carried out while maintaining a certain population diversity. The global search ability and local development ability are balanced, the average accuracy of path planning is improved, the robustness of path planning is enhanced, and the ability to obtain better path solutions is improved.

Artificial Bee Colony Algorithm for Solving Educational Timetabling Problems

2012 ◽  
Vol 3 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Asaju La’aro Bolaji ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Solution Method ◽  
Search Space ◽  
Initial Population ◽  
Timetabling Problem ◽  
Bee Colony ◽  
Educational Timetabling ◽  
Problem Instances ◽  
Uncapacitated Examination Timetabling Problem

This paper presents an artificial bee colony algorithm (ABC) for Education Timetabling Problem (ETP). It is aimed at developing a good-quality solution for the problem. The initial population of solutions was generated using Saturation Degree (SD) and Backtracking Algorithm (BA) to ensure the feasibility of the solutions. At the improvement stage in the solution method, ABC uses neighbourhood structures iteratively within the employed and onlooker bee operators, in order to rigorously navigate the UTP search space. The technique was evaluated using curriculum-based course timetabling (CB-CTT) and Uncapacitated Examination Timetabling Problem (UETP) problem instances. The experimental results on UETP showed that the technique is comparable with other state-of-the-art techniques and provides encouraging results on CB-CTT.

scholarly journals A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Wen Liu
Keyword(s):  
Convergence Rate ◽  
Learning Strategies ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Local Optimum ◽  
Simulation Experiments ◽  
Bee Colony ◽  
Search Mechanism ◽  
Improved Algorithm

Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster.

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