Jadavpur University

About: Jadavpur University is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Fuzzy logic. The organization has 10856 authors who have published 27678 publications receiving 422069 citations. The organization is also known as: JU & Jadabpur University.

A Cluster-Based Differential Evolution Algorithm With External Archive for Optimization in Dynamic Environments

Abstract: This paper presents a Cluster-based Dynamic Differential Evolution with external Ar chive (CDDE_Ar) for global optimization in dynamic fitness landscape The algorithm uses a multipopulation method where the entire population is partitioned into several clusters according to the spatial locations of the trial solutions The clusters are evolved separately using a standard differential evolution algorithm The number of clusters is an adaptive parameter, and its value is updated after a certain number of iterations Accordingly, the total population is redistributed into a new number of clusters In this way, a certain sharing of information occurs periodically during the optimization process The performance of CDDE_Ar is compared with six state-of-the-art dynamic optimizers over the moving peaks benchmark problems and dynamic optimization problem (DOP) benchmarks generated with the generalized-dynamic-benchmark-generator system for the competition and special session on dynamic optimization held under the 2009 IEEE Congress on Evolutionary Computation Experimental results indicate that CDDE_Ar can enjoy a statistically superior performance on a wide range of DOPs in comparison to some of the best known dynamic evolutionary optimizers

Noisy evolutionary optimization algorithms – A comprehensive survey

Abstract: Noisy optimization is currently receiving increasing popularity for its widespread applications in engineering optimization problems, where the objective functions are often found to be contaminated with noisy sensory measurements. In absence of knowledge of the noise-statistics, discriminating better trial solutions from the rest becomes difficult in the “selection” step of an evolutionary optimization algorithm with noisy objective/s. This paper provides a thorough survey of the present state-of-the-art research on noisy evolutionary algorithms for both single and multi-objective optimization problems. This is undertaken by incorporating one or more of the five strategies in traditional evolutionary algorithms. The strategies include (i) fitness sampling of individual trial solution, (ii) fitness estimation of noisy samples, (iii) dynamic population sizing over the generations, (iv) adaptation of the evolutionary search strategy, and (v) modification in the selection strategy.