An ant colony optimization method for generalized TSP problem

Yang, Jinhui; Shi, Xiaohu; Marchese, Maurizio; Liang, Yanchun

doi:10.1016/j.pnsc.2008.03.028

Cited by 210 publications

(92 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other hands, in the later period of PSO, we should decrease ω to enhance the local search ability. 1 c and 2 c are two so called parameters to weigh the importance of self-cognitive and social-influence, so we should take bigger 2 c and smaller 1 c in early stage， which can enhance the diversity of the swarm. On the contrary, we should take bigger 1 c and smaller 2 c to enhance the local search ability and the convergence speed in the later period.…”

Section: B Self-adaptive Pso Algorithmmentioning

confidence: 99%

“…Traditional algorithms existed for TSP such as stage dynamic programming, linear programming solution, and greedy method will get into the "combinational explosion problem" with the increase of city number. In recent years, people proposed many intelligence optimization algorithms, such as GA, ACO [1], PSO, firefly algorithm (FA) [2], and cuckoo search algorithm (CSA) [3]. Though these algorithms cannot ensure to find the best solution, they decrease the solution space and increase the probability of explores the optima.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intuitionistic Fuzzy Hybrid Discrete Particle Swarm Optimization for Solving Travelling Salesman Problem

Mei¹,

Jixue²,

Wang³

et al. 2016

Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science

View full text Add to dashboard Cite

Abstract. An intuitionistic fuzzy hybrid discrete particle swarm optimization (IF-HDPSO) is proposed for solving travelling salesman problem (TSP). By defining intuitionistic fuzzy charisma function, the IF-HDPSO algorithm exploits some other individuals to participate in the updates of velocity and position except the best one. In addition, the PSO identical factor function is defined to adjust inertia weight and learning operator adaptively, so the IF-HDPSO can explore the whole optimal solution quickly. Furthermore, an adaptive genetic algorithm based on elitist reserving strategy is developed, and combine it with PSO to reduce the probability of being trapped in the local optima and premature convergence. The simulation results indicate IF-HDPSO perform better on precision, iteration number and computational robustness.

show abstract

Section: B Self-adaptive Pso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Hybrid Discrete Particle Swarm Optimization for Solving Travelling Salesman Problem

Mei¹,

Jixue²,

Wang³

et al. 2016

Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science

View full text Add to dashboard Cite

show abstract

“…Updating: when more ants pass from the same route, the more amount of pheromone gets deposited and also it vanishes as time goes on. the pheromone evaporation are covered in [24]. The concern of ACO is sometimes trapping into a local minimum and memory limitations for large-size problems.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Optimal Generation Control of Interconnected Power System Including DFIG-Based Wind Turbine

Dahiya

Sharma

2015

IETE Journal of Research

View full text Add to dashboard Cite

The impact of wind power with higher size of penetration becomes significant for frequency control problem due to its inconsistent nature and lack of frequency support from the wind turbine units. The frequency can be managed using enhanced control of inertia and droop characteristics. However, at very low and high speeds, the adjustment of pitch angle of wind turbine is necessary so that the aerodynamic power produced remains within the designed limits. This paper discloses the design of proportional integral (PI) control algorithm to adjust pitch angle of doubly fed induction generator (DFIG)-based wind turbine (WT) integrated with two-area-interconnected power system. Here, a heuristic gravitational search algorithm (GSA) is employed to optimize the gains of PI controllers used for pitch angle adjustment and governor speed control of wind turbine, frequency regulation, and tie-line power flow control. The influence of optimal gains by GSA is compared with genetic algorithm (GA) and ant colony optimization (ACO) approaches. Finally, the effectiveness of GSA-optimized controllers is demonstrated in terms of reduced settling time, overshoot, eigenvalues, and oscillations. The competency of proposed controller is also studied by considering the variation of wind power source from 10% to 40% in the power system and also the real-time physical constraints like generation rate, time delay, and governor deadband.

show abstract

“…According to the experimental results, the algorithm in the optimal solution accuracy and convergence speed was both improved. Foreign scholars Yang Jinhui (2008) [3] and others proposed an ant colony algorithm with variation process and local search. TSP problems could be solved well by using this improved ant colony algorithm.…”

Section: The Application Of Ant Colony Algorithm In Traveling Salesmamentioning

confidence: 99%

The application status and prospect of ant colony algorithm

Guo¹,

Zhou²

2015

Proceedings of the 2015 International Symposium on Computers &Amp; Informatics

View full text Add to dashboard Cite

Ant colony algorithm is one of the representative methods of swarm intelligence. It is a kind of simulation optimization algorithm. Because it has many good qualities, many NP -hard problems have been solved successfully by using it. This article first expounds the basic theory of ant colony algorithm and makes the traveling salesman problem (TSP) an example to illustrate the basic model of ant colony algorithm. Then it summarized its application in static and dynamic combination optimization problem. Finally, the article makes the prospect of the direction of ant colony algorithm in research and application.

show abstract

An ant colony optimization method for generalized TSP problem

Cited by 210 publications

References 14 publications

Intuitionistic Fuzzy Hybrid Discrete Particle Swarm Optimization for Solving Travelling Salesman Problem

Intuitionistic Fuzzy Hybrid Discrete Particle Swarm Optimization for Solving Travelling Salesman Problem

Optimal Generation Control of Interconnected Power System Including DFIG-Based Wind Turbine

The application status and prospect of ant colony algorithm

Contact Info

Product

Resources

About