An evolutionary and cooperative agents model for optimization

Abbattista, Fabio; Abbattista, N.; Caponetti, Laura

doi:10.1109/icec.1995.487464

Cited by 22 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deng et al [6] GA + ACO + PSO Generalized TSP Wang et al [7] GA + ACO Generalized TSP Mavrovouniotis and Yang [8] GA + ACO Dynamic TSP Baraglia et al [9] Compact GA + local search (Lin-Kernighan) Generalized TSP Tsai et al [10] Parallel GA + ACO Generalized TSP Nguyen et al [11] Steady-state GA + local search (Lin-Kernighan) Generalized TSP Xing et al [12] GA + local determinate/stochastic search + global optimization Generalized TSP Lin et al [13] GA + Newton-Raphson search Generalized TSP Dong et al [14] Cooperative GA + ACO Generalized TSP Abbattista et al [15] GA + ACO Generalized TSP Lope and Coelho [16] GA + PSO Blind TSP Chen and Flann [17] GA + SA Generalized TSP Creput and Koukam [18] GA + ANN (self-organization map) Generalized TSP Jin et al [19] GA + ANN Generalized TSP Glover et al [20] GA + TS Generalized TSP Shim et al [21] GA + EDA Multiobjective TSP Marinakis et al [22] GA + GRASP Generalized TSP Chen and Chien [23] GA + SA + ACO + PSO Generalized TSP Martin and Otto [24] SA + local search (Lin-Kernighan) generalized TSP Malek et al [25] SA + TS Generalized TSP Geng et al [26] SA + greedy search Generalized TSP Pepper et al [27] SA + Monte Carlo method (demon algorithm) Generalized TSP Shim et al [28] SA + EDA + hill climbing + evolutionary gradient search Multiobjective multiple TSP Marinakis and Marinaki [29] GRASP + PSO probabilistic TSP Marinakis and Marinaki [30] GRASP + ABC + neighborhood search Generalized TSP Hernández-Pérez [31] GRASP + variable neighborhood search One-commodity TSP Marinakis and Marinaki [32] GRASP + ABC Probabilistic TSP Dai et al [33] AIS + quantum search Generalized TSP Dai et al [34] IA + bi-direction quantum search Generalized TSP Masutti and de Castro [35] IA + ANN Generalized TSP Gao et al [36] IA + ACO Generalized TSP Guo et al…”

Section: Researchersmentioning

confidence: 99%

Immune algorithm combined with estimation of distribution for traveling salesman problem

Wang

et al. 2016

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

This paper describes an artificial immune algorithm (IA) combined with estimation of distribution algorithm (EDA), named IA-EDA, for the traveling salesman problem (TSP). Two components are incorporated in IA-EDA to further improve the performance of the conventional IA. First, aiming to strengthen the information exchange during different solutions, two kinds of EDAs involving univariate marginal distribution algorithm and population-based incremental learning are altered based on the permutation representation of TSP. It is expected that new promising candidate solutions can be sampled from the constructed probabilistic model of EDA. Second, a heuristic refinement local search operator is proposed to repair the infeasible solutions sampled by EDA. Therefore, IA-EDA can alleviate the deficiencies of the conventional IA and can find better solutions for TSP by well balancing the exploitation and exploration of the search. Experiments are conducted based on a number of benchmark instances with size up to 100 000 cities. Simulation results show that IA-EDA is effective for improving the performance of the conventional IA and can produce better or competitive solutions than other hybrid algorithms. (Non-member) received the M.S. degree from the University of Toyama (UT), Toyama, Japan, in 2011. He is currently working toward the Ph.D. degree in the Faculty of Engineering, UT. His research interests include artificial immune algorithms, evolutionary algorithms, and combinatorial optimizations. Yirui Wang (Non-member) received the B.S. degree from Donghua University (DU), Shanghai, China, in 2014. He is currently working toward the M.S. degree at the College of Information Sciences and Technology, DU. His current research interests include computational intelligence, swarm intelligent algorithms, and combinatorial optimizations. Zhe Xu S153IEEJ Trans 11(S1): S142-S154 (2016) Z. XU ET AL.Sheng Li (Non-member) received the B.S. degree from Nanjing

show abstract

Section: Researchersmentioning

confidence: 99%

Immune algorithm combined with estimation of distribution for traveling salesman problem

Wang

et al. 2016

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…Fabio Abbattista proposed the optimization of the parameters Q , , β α using GA [4] . Q can no longer remain fixed, but dynamically be adjusted [5] .…”

Section: The Basic Principles Of Ant Colony Algorithmmentioning

confidence: 99%

The Improvement of Ant Colony Algorithm and Its Application to TSP Problem

Zhang

Liu

Tong

2009

2009 5th International Conference on Wireless Communications, Networking and Mobile Computing

View full text Add to dashboard Cite

The researches and applications on ant colony algorithm have made great progress in recent years. A number of results have proved the validity of the algorithm and its advantages in some fields. However, its basic shortcomings, which are long searching time and easily jumping into local optimal solution, have not been completely solved. This paper analyzes the reasons of stagnation and then introduces a new solution for avoiding stagnation, which includes the direct exchange of pheromone of some edges and dynamically setting evaporation rate for each edge. The simulation results demonstrate that the above approach is reasonable and efficient.

show abstract

“…The concept of integrating a GA and ACO was first proposed by Abbattista et al [6] for exploiting the cooperative effect of ACO with the evolutionary effect of a GA. They integrated these search methods by using GAs to evolve optimal parameter values for ACO.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

Zhao

Yao

Luan

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A novel fused algorithm that delivers the benefits of both genetic algorithms (GAs) and ant colony optimization (ACO) is proposed to solve the supplier selection problem. The proposed method combines the evolutionary effect of GAs and the cooperative effect of ACO. A GA with a great global converging rate aims to produce an initial optimum for allocating initial pheromones of ACO. An ACO with great parallelism and effective feedback is then served to obtain the optimal solution. In this paper, the approach has been applied to the supplier selection problem. By conducting a numerical experiment, parameters of ACO are optimized using a traditional method and another hybrid algorithm of a GA and ACO, and the results of the supplier selection problem demonstrate the quality and efficiency improvement of the novel fused method with optimal parameters, verifying its feasibility and effectiveness. Adopting a fused algorithm of a GA and ACO to solve the supplier selection problem is an innovative solution that presents a clear methodological contribution to optimization algorithm research and can serve as a practical approach and management reference for various companies.

show abstract

An evolutionary and cooperative agents model for optimization

Cited by 22 publications

References 3 publications

Immune algorithm combined with estimation of distribution for traveling salesman problem

Immune algorithm combined with estimation of distribution for traveling salesman problem

The Improvement of Ant Colony Algorithm and Its Application to TSP Problem

A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

Contact Info

Product

Resources

About