Multi-Objective Optimization in Construction Project Based on a Hierarchical Subpopulation Particle Swarm Optimization Algorithm
Weibo Wang, Quanyuan Feng
Abstract
Weibo Wang, Quanyuan Feng
Abstract
Comprehensive trade-off control on construction time, cost and quality is main aspect of construction project management, and it is significant for improving the benefits of construction projects. This paper presents mathematical models for time, cost and quality separately, and a multi-objective optimization model for time-cost-quality trade-off optimization is set up by synthesizing weighted single-objective models. In a case study, comparing to standard particle swarm optimization (SPSO) and differential evolution algorithm (DE), the most satisfied decision results can be obtained by applying the hierarchical subpopulation particle swarm optimization algorithm (HSPSO) proposed in this paper to solve time-cost-quality trade-off problems. Finally, exhaustive enumeration is given to verify the effectiveness of the models and the feasibility of solution method.
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Comprehensive trade-off control on construction time, cost and quality is main aspect of construction project management, and it is significant for improving the benefits of construction projects. This paper presents mathematical models for time, cost and quality separately, and a multi-objective optimization model for time-cost-quality trade-off optimization is set up by synthesizing weighted single-objective models. In a case study, comparing to standard particle swarm optimization (SPSO) and differential evolution algorithm (DE), the most satisfied decision results can be obtained by applying the hierarchical subpopulation particle swarm optimization algorithm (HSPSO) proposed in this paper to solve time-cost-quality trade-off problems. Finally, exhaustive enumeration is given to verify the effectiveness of the models and the feasibility of solution method.
Key concepts: Particle swarm optimization, Multi-swarm optimization, Mathematical optimization, Computer science, Set (abstract data type), Differential evolution, Meta-optimization, Metaheuristic