A Pareto-Optimality Based Multi-Objective Optimisation Approach to Assist Optical Network (Re-)Design Choices

2021 ◽  
Author(s):  
Sam Nallaperuma ◽  
Nikita A. Shevchenko ◽  
Seb J. Savory
Keyword(s):  
Pareto Optimality ◽  
Optical Network ◽  
Multi Objective

How Genetic Algorithms Handle Pareto-Optimality in Design and Manufacturing

2007 ◽  
pp. 465-482
Author(s):  
N. Chakraborti
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Pareto Optimality ◽  
Multi Objective Optimization ◽  
Recent Past ◽  
Multi Objective ◽  
Manufacturing Sectors ◽  
Design And Manufacturing ◽  
Basic Concepts

An informal analysis is provided for the basic concepts associated with multi-objective optimization and the notion of Pareto-optimality, particularly in the context of genetic algorithms. A number of evolutionary algorithms developed for this purpose are also briefly introduced, and finally, a number of paradigm examples are presented from the materials and manufacturing sectors, where multi-objective genetic algorithms have been successfully utilized in the recent past.

A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures

2018 ◽  
Vol 10 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Zhe Xiong ◽  
Xiao-Hui Li ◽  
Jing-Chang Liang ◽  
Li-Juan Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Pareto Optimality ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Mathematical Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Structural Problems

In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimization (PSO) and increases its robustness.

Multi objective SNP selection using pareto optimality

2013 ◽  
Vol 43 ◽  
pp. 23-28 ◽  
Author(s):  
Ergun Gumus ◽  
Zeliha Gormez ◽  
Olcay Kursun
Keyword(s):  
Pareto Optimality ◽  
Multi Objective

A Necessary Condition for Optimality in Multi Objective Programming under Inclusion Constraints

2011 ◽  
Vol 403-408 ◽  
pp. 1322-1325
Author(s):  
Jin Xing Shen
Keyword(s):  
Optimality Conditions ◽  
Optimality Condition ◽  
Pareto Optimality ◽  
Necessary Condition ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Programming ◽  
Single Objective

Optimality conditions for multi objective programming problems have been studied extensively in the literature. A necessary condition for Pareto optimality is derived by reducing the multi objective programming under inclusion constraints to systems of single objective problem and then using known results of them. The result is reasonable and efficient. Our aim in this paper is to get the optimality condition of problem (MOP) by a lemma which helps reducing multi objective optimality problem to systems of single objective ones.

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