scholarly journals A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2675 ◽  
Author(s):  
Yang Zhang ◽  
Huihui Zhao ◽  
Yuming Cao ◽  
Qinhuo Liu ◽  
Zhanfeng Shen ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Elapsed Time ◽  
Route Design

The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO) was 232.343, and the elapsed time for one solution was approximately 1.93 ms. Meanwhile, the best route selected by modified Cuckoo Search (CS) was 244.247, and the elapsed time for one solution was approximately 0.794 ms. The modified ant colony optimization algorithm can find the route with smaller fitness function value, while the modified cuckoo search algorithm can find the route overlapped to the manual selected route better. The modified cuckoo search algorithm runs more quickly but easily sticks into the premature convergence. Additionally, the best route selected by the hybrid ant colony and cuckoo search algorithm is the same as the modified ant colony optimization algorithm (232.343), but with higher efficiency and better stability.

MACOA: A Modified Ant Colony Optimization Algorithm for the CWFRD Problem

2013 ◽  
Vol 427-429 ◽  
pp. 2412-2415
Author(s):  
Yong Wang ◽  
Qiang Dou ◽  
Wei Peng ◽  
Zheng Hu Gong
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Simple Cycle ◽  
Traditional Scheduling ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling Scheme ◽  
Route Design ◽  
Closed Walk ◽  
Message Ferry

In the traditional scheduling scheme of the message ferry in wireless sensor networks, the route of a ferry is usually designed as a simple cycle. Closed Walk Ferry Route Design (CWFRD) aims to design the ferry route as a closed walk which contains more than one simple cycle to minimize the average weighted delay of the sensed data to the sink. In this paper, an Modified Ant Colony Optimization Algorithm (MACOA) is proposed to solve the CWFRD problem. The experimental results show that MACOA can greatly reduce the average weighted delay, comparing the previous proposed algorithms.

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.

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