Rohan Mukherjee

TL;DR: A multipopulation-based adaptive differential evolution (DE) algorithm to solve dynamic optimization problems (DOPs) in an efficient way that uses Brownian and adaptive quantum individuals in conjunction with the DE individuals to maintain the diversity and exploration ability of the population.

TL;DR: Covariant Matrix Adaptation with Evolution Strategy with mean learning technique (MLT) is used to solve the proposed economic dispatch problem for both conventional power system, and wind-thermal power system considering the provision for spinning reserves.

TL;DR: Experimental results indicate that CbDE-wCA can outperform other state-of-art dynamic multimodal optimizers in a statistically significant way, thereby proving its worth as an attractive alternative for niching in dynamic environments.

TL;DR: A scheme to modify the very basic framework of PSO by the introduction of a novel dimensional mean based perturbation strategy, a simple aging guideline, and a set of nonlinearly time-varying acceleration coefficients to achieve a better tradeoff between explorative and exploitative tendencies and thus to avoid premature convergence on multimodal fitness landscapes is presented.

TL;DR: A modified version of the Differential Evolution algorithm for solving Dynamic Optimization Problems (DOPs) efficiently that can outperform other algorithms for most of the tested DOP instances in a statistically meaningful way is presented.