.png){kind=icon}
Return
A multi-objective particle swarm optimization for project selection problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2009.05.056.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Selecting the most appropriate projects out of a given set of investment proposals is recognized as a critical issue for which the decision maker takes several aspects into consideration. Since many of these aspects may be conflicting, the problem is rendered as a multi-objective one. Consequently, we consider a multi-objective project selection problem in this Study where total benefits are to be maximized while total risk and total coat must be minimized, simultaneously. Since solving an NP-hard problem becomes demanding as the number of projects grows, a multi-objective particle swarm with new selection regimes for global best and personal best for swarm members is designed to find the locally Pareto-optimal frontier and is compared with a salient multi-objective genetic algorithm, i.e. SPEAII, based on some comparison metrics with random instances. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Multi-objective particle swarm
Project selection problem
Multi-objective genetic algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
Cited Papers
No cited papers available