SummaryComputational intelligence techniques have widespread applications in the field of engineering process optimization, which typically comprises of multiple conflicting objectives. An efficient hybrid algorithm for solving multi‐objective optimization, based on particle swarm optimization (PSO) and artificial bee colony optimization (ABCO) has been proposed in this paper. The novelty of this algorithm lies in allocating random initial solutions to the scout bees in the ABCO phase which are subsequently optimized in the PSO phase with respect to the velocity vector. The last phase involves loyalty decision‐making for the uncommitted bees based on the waggle dance phase of ABCO. This procedure continues for multiple generations yielding optimum results. The algorithm is applied to a real life problem of intercity route optimization comprising of conflicting objectives like minimization of travel cost, maximization of the number of tourist spots visited and minimization of the deviation from desired tour duration. Solutions have been obtained using both pareto optimality and the classical weighted sum technique. The proposed algorithm, when compared analytically and graphically with the existing ABCO algorithm, has displayed consistently better performance for fitness values as well as for standard benchmark functions and performance metrics for convergence and coverage.
A word may have multiple senses and the challenge is to find out which particular sense is appropriate in a given context. Word sense disambiguation(WSD) resolves this ambiguity by finding out which particular sense of a word is appropriate in a given context. WSD is of critical importance in the areas of machine translation, information retrieval, speech processing etc. In this paper we present some approaches to Word sense disambiguation in Nepali using Nepali WordNet. These approaches are overlap based approach and conceptual distance and semantic graph based approach which falls under Knowledge based approach. Conceptual distance and semantic graph distance are used as a measures to score our WSD algorithm.
The volume of digitized text documents on the web have been increasing rapidly. As there is huge collection of data on the web there is a need for grouping(clustering) the documents into clusters for speedy information retrieval. Clustering of documents is collection of documents into groups such that the documents within each group are similar to each other and not to documents of other groups. Quality of clustering result depends greatly on the representation of text and the clustering algorithm. This paper presents a comparative analysis of three algorithms namely K-means, Particle swarm Optimization (PSO) and hybrid PSO+K-means algorithm for clustering of text documents using WordNet. The common way of representing a text document is bag of terms. The bag of terms representation is often unsatisfactory as it does not exploit the semantics. In this paper, texts are represented in terms of synsets corresponding to a word. Bag of terms data representation of text is thus enriched with synonyms from WordNet. K-means, Particle Swarm Optimization (PSO) and hybrid PSO+K-means algorithms are applied for clustering of text in Nepali language. Experimental evaluation is performed by using intra cluster similarity and inter cluster similarity. .
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