This paper aims to introduce an improved coordinated aggregation-based particle swarm optimization (ICA-PSO) algorithm for solving the optimal economic load dispatch (ELD) problem in power systems. ...In the ICA-PSO algorithm each particle in the swarm retains a memory of its best position ever encountered, and is attracted only by other particles with better achievements than its own with the exception of the particle with the best achievement, which moves randomly. Moreover, the population size is increased adaptively, the number of search intervals for the particles is selected adaptively and the particles search the decision space with accuracy up to two digit points resulting in the improved convergence of the process.
A Hybrid PBIL-Based Krill Herd Algorithm Gai-Ge Wang; Deb, Suash; Gandomi, Amir H. ...
2015 3rd International Symposium on Computational and Business Intelligence (ISCBI),
12/2015
Conference Proceeding
When krill herd (KH) is used to solve complicated multimodal functions, sometimes it fails to find the best solutions and cannot converge fast. Herein, we propose a hybrid KH method, called PBILKH, ...by integrating the KH with the population-based incremental learning (PBIL). In addition, a type of elitism is applied to memorize the krill with the best fitness when finding the best solution. The effectiveness of the PBILKH is verified by various benchmarks and experimental results demonstrate that our PBILKH is well capable of overtaking the KH algorithm and other optimization methods in solving nonlinear problems.
This study proposes a novel chaotic cuckoo search (CCS) optimization method by introducing chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS ...with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS in order to preserve the best cuckoos. In the CCS method, twelve chaotic maps are applied to tune the step size of the cuckoos used in the original CS method. Twenty-two benchmark functions are utilized to investigate the efficiency of CCS. The results show that the performance of CCS together with a suitable chaotic map are comparable as well as superior to that of the CS and other metaheuristic algorithms.
Matching identical products present in multiple product feeds constitutes a crucial element of many tasks of e-commerce, such as comparing product offerings, dynamic price optimization, and selecting ...the assortment personalized for the client. It corresponds to the well-known machine learning task of entity matching, with its own specificity, like omnipresent unstructured data or inaccurate and inconsistent product descriptions. This paper aims to present a new philosophy to product matching utilizing a semi-supervised clustering approach. We study the properties of this method by experimenting with the IDEC algorithm on the real-world dataset using predominantly textual features and fuzzy string matching, with more standard approaches as a point of reference. Encouraging results show that unsupervised matching, enriched with a small annotated sample of product links, could be a possible alternative to the dominant supervised strategy, requiring extensive manual data labeling.
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