New optimization techniques in engineering

By Godfrey C. Onwubolu, B. Babu. Studies in Fuzziness and Soft Computing, 2004

599 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e03743c7eb44df231054365697459f404a201f19

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In Artificial Intelligence

Citations in the corpus

Butterfly optimization algorithm: a novel approach for global optimization (2018, methodology): “…these biological systems, various metaheuristic algorithms have been developed and employed to various optimization problems, whereas the conventional optimization algorithms fail to produce satisfactory results for problems with nonlinearity and multimodality (Back 1996; Onwubolu and Babu 2004).”
Biogeography-Based Optimization (2008, methodology): “ACO [14]‐[ 17 ] is an algorithm that is based on the pheromone deposition of ants.”
Opposition-Based Differential Evolution (2008, background): “Differential evolution (DE) is a population-based and directed search method [25], [26].”
Opposition versus randomness in soft computing techniques (2008, background): “Many optimization methods need to generate random numbers for initialization or using them during the search process, such as differential evolution (DE) [36], genetic algorithms (GAs) [20], ant colony optimization (ACO) [21], particle swarm optimization (PSO) [22], simulated annealing (SA) [23], an”
Quasi-oppositional Differential Evolution (2007, methodology): “Differential Evolution (DE) is a population-based and directed search method [6], [7].”
A discrete differential evolution algorithm for the permutation flowshop scheduling problem (2007, methodology): “Since DE was first introduced to solve the Chebychev polynomial fitting problem by Storn & Price [24], it has been successfully applied in a variety of applications that can be found in Price et al. [25] and Babu & Onwubolu [26].”
A novel population initialization method for accelerating evolutionary algorithms (2007, methodology): “For classical DE, the procedure is as follows [13]:”