Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques

Selvi, V. Agnes Idhaya; Umarani, R.

doi:10.5120/908-1286

Cited by 158 publications

(82 citation statements)

References 3 publications

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“…Particle swarm optimization algorithm was introduced by Kennedy and Eberhart in 1995 [19], [20]. The algorithm consists of a swarm of particles flying through the search space.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimizing Back-Propagation using PSO_Hill_A* and Genetic Algorithm

Sharma¹,

Mishra²

2013

IJCA

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Back propagation(BP) is used to solve real world problems which use the concept of multilayer perceptron(MLP). BP have the disadvantage of trapped in local minima, slow convergence rate and more error prone. To optimize BP Algorithm, Particle swarm optimization(PSO) and Genetic algorithm(GA) is used. Limitation of PSO_Hill and PSO_A* is overcomes when these algorithms are combined and on the basis of strength of these two algorithm we proposed a new PSO_Hill_A* algorithm which is used to optimize and enhance learning process in terms of convergence rate and accuracy. GA is a kind of method to simulate and to search the optimal solution, GA can have four operations including Encoding, selecting, crossover and Mutation. To optimize and improve BP, we proposed two architecture: 1) Use of PSO_Hill_A* before and after hidden layer. 2) Use of GA before and after hidden layer.

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“…Particle swarm optimization algorithm was introduced by Kennedy and Eberhart in 1995 [19], [20]. The algorithm consists of a swarm of particles flying through the search space.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimizing Back-Propagation using PSO_Hill_A* and Genetic Algorithm

Sharma¹,

Mishra²

2013

IJCA

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“…This algorithm is reported to guarantee the convergence; however the time to converge is inconsistent [54]. Besides, as this algorithm is developed based on routing problems, the theoretical is reported to be quite difficult to be applied in other applications.…”

Section: Optimization Of Lssvm Using Evolutionary Computationmentioning

confidence: 99%

A Review on Optimization of Least Squares Support Vector Machine for Time Series Forecasting

Yusof¹,

Mustaffa²

2016

IJAIA

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Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters based on two main classes; Evolutionary Computation and Cross Validation.

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“…Furthermore, ACO is suitable for scheduling the dynamic problems. In ACO, although solution search space convergence is guaranteed, however, time to convergence is uncertain [65]. Generally due to the limitations of the ACO, scheduling jobs on computational grid using ACO produces good but not optimal schedules in term of makespan time and flowtimes [14,66,67].…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%