scholarly journals Maximized Users Influence in Social Networks by using Hybrid Modified Firefly with Ant Colony Optimization Algorithm

2018 ◽  
Vol 8 (3) ◽  
pp. 6899-6904
Keyword(s):  
Optimization Algorithm ◽  
Social System ◽  
Firefly Algorithm ◽  
Additional Data ◽  
Optimization Methods ◽  
Neighborhood Search ◽  
Ant Colony Optimization Algorithm ◽  
Conventional Models ◽  
Unconstrained Nonlinear Optimization ◽  
Modified Firefly Algorithm

Influence expansion issue is to locate a lot of seeds in informal communities with the end goal that the course influence is boosted. Conventional models expect that all hubs are eager to spread the influence once they are influenced, and they overlook the divergence among influence and benefit of an item. Suggestion frameworks have gotten significant consideration as of late. Be that as it may, most research has been centered around improving the presentation of collective separating procedures. Interpersonal organizations, crucially, give us additional data on individuals' inclinations, and ought to be considered and conveyed to improve the nature of suggestions. This paper displays a hybrid algorithm joining two heuristic optimization methods, ACO (Ant Colnoly Optimization) and HMFA (Hybrid Modified Firefly Algorithm) for amplifying Social system clients influence level.. The proposed algorithm coordinates the benefits of both ACO and HMFA, where the algorithm is introduced by a lot of arbitrary ants that is meandering through the inquiry space. During this wandering a development of these ants is performed by coordinating ACO and HMFA, where HMFA fills in as a neighborhood search to refine the positions found by the ants. Then again, the exhibition of HMFA is improved by lessening the randomization parameter with the goal that it diminishes slowly as the optima are drawing nearer. The examinations of numerical outcomes demonstrate that there is an extent of research in hybridizing swarm insight techniques to tackle troublesome persistent optimization issues and this hybrid ACO–HMFA is a promising and profitable device to take care of unconstrained nonlinear optimization issues. A cautious perception will uncover the accompanying advantages of the proposed optimization algorithm.

Adaptive cooperative ant colony optimization algorithm based on reinforcement incentive and its application

2021 ◽  
pp. 1-10
Author(s):  
Weiwei Yu ◽  
Chengwang Xie ◽  
Chao Deng
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Information Exchange ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm

Ant colony algorithm has great advantages in solving some NP complete problems, but it also has some problems such as slow search speed, low convergence accuracy and easy to fall into local optimum. In order to balance the contradiction between the convergence accuracy and the convergence speed of ant colony algorithm, this paper first proposes an ant colony algorithm (RIACO) based on the reinforcement excitation theory of Burrus Frederic Skinner. In this algorithm, pheromone is stimulated and its volatilization coefficient is adjusted adaptively according to the iteration times, thus the speed of ant colony search is accelerated. Secondly, based on the characteristics of real ant colony classification and division of labor, this paper proposes an ant colony algorithm based on labor division and cooperation (LCACO). The algorithm divides the ant colony into two different types of ant colony for information exchange and improves the state transition probability formula, so that the two ant colonies can search the optimal path cooperatively, so as to improve the precision of ant colony search. Finally, combining the two improved ant colony algorithms, this paper proposes an adaptive cooperative ant colony optimization algorithm based on reinforcement incentive (SMCAACO). A multi constrained vehicle routing problem (MCVRP) is compared with the classical tabu search algorithm (TS), variable neighborhood search algorithm (VNS) and basic ant colony algorithm (ACO). The experimental results show that, in solving the mcvrp problem, the algorithm proposed in this paper not only has a good performance in the solution results, but also achieves a good balance between the convergence speed and the convergence accuracy.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

Sign in / Sign up

Export Citation Format

Share Document