Open AccessVector‐angle penalised NSGA‐III to solve many‐objective optimisation problems
Author(s) -
Gupta R.,
Nanda S.J.
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2018.7164
Subject(s) - sorting , mathematical optimization , convergence (economics) , genetic algorithm , population , set (abstract data type) , multi objective optimization , mathematics , evolutionary algorithm , computer science , point (geometry) , algorithm , demography , geometry , sociology , economics , programming language , economic growth
One of the major challenges in evolutionary many‐objective optimisation is to maintain convergence and diversity among Pareto‐optimal solutions. Taking both into consideration, this Letter presents a θ ‐NSGA‐III algorithm which incorporates minimum‐vector‐angle principle in association operation of original non‐dominated sorting genetic algorithm III (NSGA‐III) scheme to solve unconstrained many‐objective optimisation problems. Each non‐dominated population member close to a reference point is emphasised in optimal solution set using minimum vector‐angle penalty parameter with perpendicular distance in association operation. Performance evaluation of θ ‐NSGA‐III algorithm is done over unconstrained DTLZ test suite by computing delta ( Δ ) and inverted generational distance as quality metrics. The improved performance of the suggested algorithm over NSGA‐III, MOEA/D and VaEA could be considered as an alternative tool to handle optimisation problems with more than three conflicting objectives.