scholarly journals AI-Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Banteng Liu ◽  
Junjie Lu ◽  
Yourong Chen ◽  
Ping Sun ◽  
Kehua Zhao ◽  
...  
Keyword(s):  
Flood Control ◽  
Artificial Bee Colony ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Scheduling Optimization ◽  
Satisfaction Rate ◽  
Bee Colony ◽  
Transportation Distance ◽  
Nondominated Solution ◽  
Nondominated Sorting

Considering the competition between rescue points, we use artificial intelligence (AI) driven Internet of Thing (IoT) and regional material storage data to propose a multiobjective scheduling algorithm of flood control materials based on Pareto artificial bee colony (MSA_PABC). To address the scheduling of flood control materials, the multiple types of flood control materials, the multiple disaster sites, and entertain both emergency and fairness of rescue need to be considered comprehensively. The MSA_PABC has the constraints such as storage quantity constraint of warehouse materials, material demand constraint, and maximum transportation distance of flood control materials. We establish the scheduling optimization model of flood control materials for each disaster rescue point and the total scheduling optimization model for all flood control materials. Then, MSA_PABC uses the modified Pareto artificial bee colony algorithm to solve the multiobjective models. Three types of initialization strategies are proposed to calculate the fitness of each rescue point and the overall evaluation value of the food source. We propose the employ bee operations such as niche technology and local search of the variable neighborhood, the onlooker bee operations such as Pareto nondominated sorting and crossover operation, the scout bee operations such as maximum evolutionary threshold, and end elimination mechanism. Finally, our proposed solution obtains the nondominated solution set and its optimal solution. The experimental results show that no matter how the number of rescue points changes, MSA_PABC can find the nondominated solution set and optimal solution quickly. It improves the convergence rate of MSA_PABC and material satisfaction rate. Our solution also reduces the average maximum transportation distance, the standard deviation of maximum transportation distance, and the standard deviation of material satisfaction rate. The evaluation also demonstrates MSA_PABC outperforms the state-of-arts such as ABC (artificial bee colony), NSGA2 (nondominated sorting genetic algorithm 2), and MOPSO (multiobjective particle swarm optimization).

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

Stellar Mass Black Hole for Engineering Optimization

2017 ◽  
pp. 62-90
Author(s):  
Premalatha Kandhasamy ◽  
Balamurugan R ◽  
Kannimuthu S
Keyword(s):  
Black Hole ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Stellar Mass ◽  
Bee Colony ◽  
Nature Inspired Algorithms ◽  
The Universe

In recent years, nature-inspired algorithms have been popular due to the fact that many real-world optimization problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to develop an optimization method whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. A black hole is an object that has enough masses in a small enough volume that its gravitational force is strong enough to prevent light or anything else from escaping. Stellar mass Black hole Optimization (SBO) is a novel optimization algorithm inspired from the property of the gravity's relentless pull of black holes which are presented in the Universe. In this paper SBO algorithm is tested on benchmark optimization test functions and compared with the Cuckoo Search, Particle Swarm Optimization and Artificial Bee Colony systems. The experiment results show that the SBO outperforms the existing methods.

An Intelligent Traffic Engineering Method over Software Defined Networks for Video Surveillance Systems Based on Artificial Bee Colony

2016 ◽  
Vol 12 (4) ◽  
pp. 45-62 ◽  
Author(s):  
Reza Mohammadi ◽  
Reza Javidan
Keyword(s):  
Video Surveillance ◽  
Traffic Engineering ◽  
Surveillance System ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Video Data ◽  
Surveillance Systems ◽  
Bee Colony

In applications such as video surveillance systems, cameras transmit video data streams through network in which quality of received video should be assured. Traditional IP based networks cannot guarantee the required Quality of Service (QoS) for such applications. Nowadays, Software Defined Network (SDN) is a popular technology, which assists network management using computer programs. In this paper, a new SDN-based video surveillance system infrastructure is proposed to apply desire traffic engineering for practical video surveillance applications. To keep the quality of received videos adaptively, usually Constraint Shortest Path (CSP) problem is used which is a NP-complete problem. Hence, heuristic algorithms are suitable candidate for solving such problem. This paper models streaming video data on a surveillance system as a CSP problem, and proposes an artificial bee colony (ABC) algorithm to find optimal solution to manage the network adaptively and guarantee the required QoS. The simulation results show the effectiveness of the proposed method in terms of QoS metrics.

Artificial Bee Colony Algorithm for An Optimal Solution for Combined Economic and Emission Dispatch Problem

2016 ◽  
Vol 5 (3) ◽  
pp. 111
Author(s):  
Sandeep Bhongade ◽  
Sourabh Agarwal
Keyword(s):  
Artificial Bee Colony ◽  
Thermal Power ◽  
Optimal Solution ◽  
Green Energy ◽  
Oxides Of Nitrogen ◽  
Fuel Cost ◽  
Geothermal Heat ◽  
Power Stations ◽  
Bee Colony ◽  
Emission Dispatch

In India Electrical Energy is generated mainly Coal based Thermal Power stations and hydro Electric Power Stations. The main aim of power generating company is to provide good quality and reliable power to consumers at minimum cost. The problem of Combined Economic and Emission Dispatch  deals with the minimization of both fuel cost and emission of pollutants such as oxides of Nitrogen and Oxides of Sulphur. In our power system the emission is major problem created that’s why in now a days we move from green energy source or renewable energy such as Sunlight, Wind, Tides, Wave, and Geothermal Heat Energy. The Emission constrained Economic Dispatch problem treats the emission limit as an additional constraint and optimizes the fuel cost. In this paper we optimizes the Combined Economic and Emission Dispatch problem by using two different optimization method such as Artificial Bee Colony (ABC) and Genetic Algorithm (GA).The proposed ABC Algorithm has been successfully implemented is to IEEE 30 bus and Indian Utility sixty two Bus System The simulation result are compare and found the effective algorithm for Combined Economic and Emission Dispatch problem.

scholarly journals Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

2017 ◽  
Vol 24 (s3) ◽  
pp. 65-71
Author(s):  
Jianjun Li ◽  
Ru Bo Zhang
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Artificial Bee Colony ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Distributed Task Allocation

Abstract The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem. A differential evolution quantum artificial bee colony (DEQABC) optimization algorithm is proposed to solve the multi-AUV optimal task allocation scheme. The algorithm is based on the quantum artificial bee colony algorithm, and it takes advantage of the characteristics of the differential evolution algorithm. This algorithm can remember the individual optimal solution in the population evolution and internal information sharing in groups and obtain the optimal solution through competition and cooperation among individuals in a population. Finally, a simulation experiment was performed to evaluate the distributed task allocation performance of the differential evolution quantum bee colony optimization algorithm. The simulation results demonstrate that the DEQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The DEQABC algorithm can effectively improve AUV distributed multi-tasking performance.

Improved Artificial Bee Colony Clustering Algorithm Based on K-Means

2014 ◽  
Vol 556-562 ◽  
pp. 3852-3855 ◽  
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Bee Colony ◽  
The Poor ◽  
Algorithm Stability ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Clustering Center

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. Artificial Bee Colony algorithm based on K-means was introduced in this article, then put forward an improved Artificial Bee Colony algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved algorithm stability of k-means clustering algorithm well, and more effectively improved clustering quality and property.

Artificial Bee Colony-Based Influence Maximization Approach for Social Coding Portal GitHub

2019 ◽  
Vol 10 (2) ◽  
pp. 34-47
Author(s):  
Anuja Arora ◽  
Riyu Bana ◽  
Habib Shah ◽  
Divakar Yadav
Keyword(s):  
Social Media ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Influence Maximization ◽  
Social Community ◽  
Social Bees ◽  
Bee Colony ◽  
Egocentric Network ◽  
Post Marketing ◽  
Community Connectivity

Influence maximization is the main source of virality of any social media post/marketing activity. In recent trends, influence maximization has moved towards analytic approach instead of just being a suggestive metaphor for various social media paradigm. In this article, ego-centric approach and a bio-inspired algorithm is applied on social coding community, Github, for influence maximization. First, developers' and projects' egocentric network-based studies are conducted to find out influential developer and project based on varying social media measures. Second, artificial bee colony (ABC) bio-inspired algorithm is used to select social bees (i.e., developers set and projects set in rapid convergence towards an optimal solution to achieve influence maximization). Algorithm result ensures the best solution in terms of social community connectivity path optimization in less run time while finding most influential social bees.

Application of discrete artificial bee colony algorithm for cloud task optimization scheduling

2020 ◽  
Vol 11 (04) ◽  
pp. 2050034
Author(s):  
Shuai Man ◽  
Rongjie Yang
Keyword(s):  
Task Scheduling ◽  
Waiting Time ◽  
Load Balance ◽  
Artificial Bee Colony ◽  
Scheduling Algorithm ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Task Scheduling Algorithm ◽  
Overall Performance ◽  
The Mathematical Model

The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system. This study mainly analyzed the application of the artificial bee colony (ABC) algorithm in the cloud task scheduling. In order to solve the problem of cloud task scheduling, the ABC algorithm was discretized to get the discrete artificial bee colony (DABC) algorithm. Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm. Finally, the simulation experiment was carried out, and the performance of first-come-first-served (FCFS), MIN–MIN, ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm. The results showed that the user waiting time of the DABC algorithm was 1210s, the load balance degree was 0.01, and the user payment fee was 1688 yuan when the number of cloud tasks was 500; compared with other algorithms, the user waiting time of the DABC algorithm was shorter, the resource load balance degree was higher, and the overall performance was better. The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling, and it can be further extended and applied in practice.

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