Multi-objective shape optimization using ant colony coupled computational fluid dynamics solver

Kumar, Gulshan; Mahendra, A.K.; Gouthaman, G.

doi:10.1016/j.compfluid.2011.01.016

Cited by 6 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The non-dominated solutions of the Pareto front are enumerated as ND = {(1, 25), (2,19), (3,17), (4,12), (5, 9.5), (6,8), (7, 7.45), (8, 6.35 Table 2). Now the ranking of the Pareto optimal solutions is made by comparing the Tchebycheff value and the weight vector.…”

Section: Stepsmentioning

confidence: 99%

See 1 more Smart Citation

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

Mishra

Dehuri

Cho

2015

Multi-Objective Swarm Intelligence

View full text Add to dashboard Cite

This paper systematically presents the Swarm Intelligence (SI) methods for optimization of multiple and many objective problems. The fundamental difference of Multiple and Many Objective Optimization problems have been studied very rigorously. The three forefront swarm intelligence methods, i.e., Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimization (ABC) has been deeply studied to understand their ways of solving multiple and many objective problems distinctly. A pragmatic topical study on the behavior of real ants, bird flocks, and honey bees in solving EEG signal analysis completes the survey followed by discussion and extensive number of relevant references.

show abstract

Section: Stepsmentioning

confidence: 99%

“…Some of the practical optimization problems include flight rescheduling [2], shape optimization [3], mobile network design [4], and minimization of shooting failure probability in the weapon target assignment [5], etc. In practice, the number of objectives in a multiple optimization problem are restricted with ≤3.…”

Section: Introductionmentioning

confidence: 99%