A comparison study of Hill Climbing, Simulated Annealing and Genetic Algorithm for node placement problem in WMNs

PurposeThe purpose of this paper is to implement a Web interface for hybrid intelligent systems. Using the implemented Web interface, this paper evaluates two hybrid intelligent systems based on particle swarm optimization, hill climbing and distributed genetic algorithm to solve the node placement problem in wireless mesh networks (WMNs).Design/methodology/approachThe node placement problem in WMNs is well-known to be a computationally hard problem. Therefore, the authors use intelligent algorithms to solve this problem. The implemented systems are intelligent systems based on meta-heuristics algorithms: Particle Swarm Optimization (PSO), Hill Climbing (HC) and Distributed Genetic Algorithm (DGA). The authors implement two hybrid intelligent systems: WMN-PSODGA and WMN-PSOHC-DGA.FindingsThe authors carried out simulations using the implemented Web interface. From the simulations results, it was found that the WMN-PSOHC-DGA system has a better performance compared with the WMN-PSODGA system.Research limitations/implicationsFor simulations, the authors considered Normal distribution of mesh clients. In the future, the authors need to consider different client distributions, patterns, number of mesh nodes and communication distance.Originality/valueIn this research work, the authors implemented a Web interface for hybrid intelligent systems. The implemented interface can be extended for other metaheuristic algorithms.

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Optimization Methods for Estimation of Missing Data

Computational Intelligence for Missing Data Imputation, Estimation, and Management ◽

10.4018/978-1-60566-336-4.ch010 ◽

2010 ◽

pp. 210-232

Author(s):

Tshilidzi Marwala

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Simulated Annealing ◽

Missing Data ◽

Missing Values ◽

Particle Swarm ◽

Optimization Methods ◽

Hill Climbing ◽

Swarm Optimization ◽

Design Variables

This chapter presents various optimization methods to optimize the missing data error equation, which is made out of the autoassociative neural networks with missing values as design variables. The four optimization techniques that are used are: genetic algorithm, particle swarm optimization, hill climbing and simulated annealing. These optimization methods are tested on two datasets, namely, the beer taster dataset and the fault identification dataset. The results that are obtained are then compared. For these datasets, the results indicate that genetic algorithm approach produced the highest accuracy when compared to simulated annealing and particle swarm optimization. However, the results of these four optimization methods are the same order of magnitude while hill climbing produces the lowest accuracy.

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A STUDY OF HEURISTIC APPROACHES FOR BREAKING SHORT CRYPTOGRAMS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213004001417 ◽

2004 ◽

Vol 13 (01) ◽

pp. 45-64 ◽

Cited By ~ 4

Author(s):

RALPH MORELLI ◽

RALPH WALDE ◽

WILLIAM SERVOS

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Simulated Annealing ◽

Heuristic Search ◽

Optimization Algorithms ◽

Hill Climbing ◽

Frequency Data ◽

Heuristic Approaches ◽

Search Heuristics

In this study, we compare the use of genetic algorithms (GAs) and other forms of heuristic search in the cryptanalysis of short cryptograms. This paper expands on the work presented at FLAIRS-2003, which established the feasibility of a word-based genetic algorithm (GA) for analyzing short cryptograms.1 In this study the following search heuristics are compared both theoretically and experimentally: hill-climbing, simulated annealing, word-based and frequency-based genetic algorithms. Although the results reported apply to substitution ciphers in general, we focus in particular on short substitution cryptograms, such as the kind found in newspapers and puzzle books. Short cryptograms present a more challenging form of the problem. The word-based approach uses a relatively small dictionary of frequent words. The frequency-based approaches use frequency data for 2-, 3- and 4-letter sequences. The study shows that all of the optimization algorithms are successful at breaking short cryptograms, but perhaps more significantly, the most important factor in their success appears to be the choice of fitness measure employed.

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