ant colony replacement recloser
8562828
Railway platform reallocation after dynamic perturbations using ant colony optimisation
Train delays at stations are a common occurrence in complex, busy railway networks. A delayed train will miss its scheduled time slot on the platform and may have to be reallocated to a new platform to allow it to continue its journey. The problem is a dynamic one because while reallocating a delayed train further unanticipated train delays may occur, changing the nature of the problem over time. Our aim in this study is to apply ant colony optimisation (ACO) to a dynamic platform reallocation problem (DPRP) using a model created from real world train schedule data. To ensure that trains are not unnecessarily reallocated to new platforms we introduce a novel best ant replacement scheme that takes into account not only the objective value but also the physical distance between the original and the new platforms. Results showed that the ACO algorithm outperformed a heuristic that places the delayed train in the first available time slot and that this improvement was more apparent with high frequency dynamic changes.
2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Jayne Eaton Shengxiang Yang
65342756
Railway platform reallocation after dynamic perturbations using ant colony optimisation
Train delays at stations are a common occurrence in complex, busy railway networks. A delayed train will miss its scheduled time slot on the platform and may have to be reallocated to a new platform to allow it to continue its journey. The problem is a dynamic one because while reallocating a delayed train further unanticipated train delays may occur, changing the nature of the problem over time. Our aim in this study is to apply ant colony optimisation (ACO) to a dynamic platform reallocation problem (DPRP) using a model created from real world train schedule data. To ensure that trains are not unnecessarily reallocated to new platforms we introduce a novel best ant replacement scheme that takes into account not only the objective value but also the physical distance between the original and the new platforms. Results showed that the ACO algorithm outperformed a heuristic that places the delayed train in the first available time slot and that this improvement was more apparent with high frequency dynamic changes.
Eaton Jayne Yang Shengxiang
9617555
Reliability Constrained Optimum Recloser Placement in Distributed Generation Using Ant Colony System Algorithm
Optimal placement of protection devices in radial feeders is important to ensure power system reliability. Distributed generation is being adopted in distribution networks with one of the objectives being enhancement of system reliability. In this paper, an ant colony system (ACS) algorithm is used to derive the optimal recloser placement scheme for a distributed generation enhanced feeder. A composite reliability index is defined as the objective function in the optimization procedure. Simulation work is carried out based on a 69 segment 8 lateral distribution feeder with distributed generation to validate the effectiveness of the proposed method
2006 IEEE PES Power Systems Conference and Exposition
Lingfeng Wang Chanan Singh
14574920
Dynamic Travel Path Optimization System Using Ant Colony Optimization
This paper demonstrates that ant colony optimization can efficiently improve the traffic situation in an urban environment. A Dynamic Travel Path Optimization System (DTPOS) based on Ant Colony Optimization (ACO) is proposed for the prediction of the best path to a given destination. In DTPOS, traffic factors such as average travel speed, average waiting time of cars and number of stopped cars in queue are taken into consideration. The proposed method is modeled in Net Logo. The simulation results demonstrate that the DTPOS model can greatly reduce the average travel time of cars in urban cases and improves the mean travel time by 47 percent when compared to similar models where the cars select their path without ACO. It has also been shown that the results can be further improved by 56 percent when the Previous Path Replacement (PPR) method is applied to the DTPOS results.
2014 UKSim AMSS 16th International Conference on Computer Modelling and Simulation
Jerry John Kponyo Yujun Kuang Enzhan Zhang
12381218
Ant colony optimization with memory based immigrants for the dynamic vehicle routing problem
A recent integration showed that ant colony optimization (ACO) algorithms with immigrants schemes perform well on different variations of the dynamic travelling salesman problem. In this paper, we address ACO for the dynamic vehicle routing problem (DVRP) with traffic factor where the changes occur in a cyclic pattern. In other words, previous environments will re appear in the future. Memory based immigrants are used with ACO in order to collect the best solutions from the environments and use them to generate diversity and transfer knowledge when a dynamic change occurs. The results show that the proposed algorithm, with an appropriate size of memory and immigrant replacement rate, outperforms other peer ACO algorithms on different DVRP test cases.
2012 IEEE Congress on Evolutionary Computation
Michalis Mavrovouniotis Shengxiang Yang
20318103
BIDIRECTIONAL SHIFTS IN COLONY QUEEN NUMBER IN A SOCIALLY POLYMORPHIC ANT POPULATION
The breeding system of social organisms affects many important aspects of social life. Some species vary greatly in the number of breeders per group, but the mechanisms and selective pressures contributing to the maintenance of this polymorphism in social structure remain poorly understood. Here, we take advantage of a genetic dataset that spans 15 years to investigate the dynamics of colony queen number within a socially polymorphic ant species. Our study population of Formica selysi has single and multiple queen colonies. We found that the social structure of this species is somewhat flexible: on average, each year 3.2% of the single queen colonies became polygynous, and conversely 1.4% of the multiple queen colonies became monogynous. The annualized queen replacement rates were 10.3% and 11.9% for single and multiple queen colonies, respectively. New queens were often but not always related to previous colony members. At the population level, the social polymorphism appeared stable. There was no genetic differentiation between single and multiple queen colonies at eight microsatellite loci, suggesting ongoing gene flow between social forms. Overall, the regular and bidirectional changes in queen number indicate that social structure is a labile trait in F. selysi, with neither form being favored within a time frame of 15 years.
Evolution; international journal of organic evolution
Jessica Purcell Michel Chapuisat
64076971
An Improved Ant Colony System for Solving1 PDTSP
An improved ant colony system optimization algorithm is proposed to solve One Commodity Pickup andDelivery Traveling Salesman Problem.Firstly,the initializing method and updating rules of pheromone in ACS are improved according to the characteristic of 1 PDTSP;next,the stop phenomenon is solved by adopting the principle of"current optimal replacement";straight after,to improve the solution quality and accelerate the convergence speed of algorithm,a variable neighborhood search algorithm with the capacity constraint is designed.And eventually,the experimental results indicate the improved ant colony system boasts its high precision and nice stability in solving 1 PDTSP.
Fu Hui
112358967
Optimization of Process Based on Adaptive Ant Colony Algorithm
To solve the optimal process planning problem in the computer aided process planning,an optimal process planning method based on adaptive ant colony algorithm(AACA) and taken minimal replacement rate of manufacturing resources as optimization objective is proposed.The manufacturing feature is sub divided into manufacturing procedures according accuracy requirement,and then the concept of machining cell which is composed of manufacturing feature,manufacturing procedure,manufacturing method,manufacturing resource and fixing location is introduced.Therefore the process planning could be represented by an optimal arrangement to the machining cells.A matrix of constraints to Machining cells is constructed according the constraints of geometrical location among manufacturing features and the priority among manufacturing procedures.The least replacement rate of manufacturing resources,which represent unified seeking for less time to market,higher quality and lower cost,is taken as the optimization objective.Under the constraints of the matrix of constraints to machining cells and the available manufacturing resources,the AACA is adopted to solve the model.An example is given to demonstrate that our method is reliable and effective to produce the process plan in accordance with the real practice.
Wei Hai-feng
146112907
Prototype Aplikasi Penjadwalan Manning Agent Crew Kapal Tanker Menggunakan Algoritma Ant Colony Optimization: Study Kasus PT Scorpa Pranedya
Agent Manning in management is certainly not easy to manage hundreds Crew of various levels against the background of different and diverse certifications In an interview with the author of a work colleague at the author found many obstacles in the face of five run ship and each the ship has 21 crew who had to standby 40% of the total crew is extremely difficult to schedule the crew when to sail and when to be off while the methods used manually using Microsof office program is Microsoft Excel With the system First off First Join author will build the system information by implementing Ant Colony Optimization algorithm, so that the crew and the tanker did not experience any schedule Ant Colony Optimization Algorithm is a kind of meta heuristic algorithms that have been proven to be able to resolve a lot of problems that are difficult combinatorial algorithm mimics the behavior of ants when the They were In a colony to find a source of food ant algorithm focuses on behavior or habit that is often done (bypassed) by ant with the ability and uniqueness of ants, which have the ability naturally (real ant) to find a path or shortest path from the nest to a source of food without understanding the visual (sight) information systems that are built using Ant Colony Optimization algorithm is capable of generating scheduling assignment by the willingness of the tanker crew and able to provide a replacement crew schedule information for the next period.
Karjono Karjono Riza Dewa Santosa
73608993
COLONY MUTATED ANT SYSTEM FOR PIPE NETWORK OPTIMIZATION
A new ant algorithm, namely Colony Mutated Ant System (CMAS) circumventing the premature convergence phenomenon is proposed in this paper and applied to pipe network optimization problems. The method uses a simple but effective mechanism, namely Pheromone Replacement Mechanism (PRM) to make sure that the global best solution path always has the maximum trail intensity. This mechanism introduces enough exploitation into the method and, more importantly, enables one to exactly predict the number of global best solutions at each iteration of the algorithm without the necessity of calculating the cost of the solutions created. This number is used as a measure for premature convergence of the method at each iteration. The colony is then mutated such that a predefined number of global best solutions survive the mutation process. Two different mutation mechanisms, namely one bit and uniform mutation are introduced and used. The probability of mutation is adjusted at each iteration so that the required number of global best solutions survive the mutation. The method is shown to produce results comparable to Max Min ant system (MMAS) algorithm, while requiring less free parameter tuning. The application of the method to a benchmark example in the pipe network optimization discipline is presented and the results are compared. The results indicate that the proposed CMAS method shows improved performance with improved convergence characteristics. Furthermore, the method requires less computational effort for tuning purposes due to the fewer number of free parameters compared to the MMAS method. Keywords Mutated, ant colony optimization, pipe networks, optimal design
M H Afshar