Pareto Front Particle Swarm Optimizer for Discrete Time-Cost Trade-Off Problem

Date

2017-01-01

Author

Aminbakhsh, Saman
Sönmez, Rifat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

168
views

0
downloads

Intensive heuristic and metaheuristic research efforts have focused on the Pareto front optimization of discrete time-cost trade-off problem (DTCTP). However, very little success has been achieved in solving the problem for medium and large-scale projects. This paper presents a new particle swarm optimization method to achieve an advancement in the Pareto front optimization of medium and large-scale construction projects. The proposed Pareto front particle swarm optimizer (PFPSO) is based on a multiobjective optimization environment with novel particle representation, initialization, and position-updating principles that are specifically designed for simultaneous time-cost optimization of large-scale projects. PFPSO brings several benefits for the discrete time-cost optimization, such as an adequate representation of the discrete search space, fast convergence properties, and improved Pareto front optimization capabilities. The computational experiment results reveal that the new particle swarm optimization method outperforms the state-of-the-art methods, both in terms of the number of Pareto front solutions and computation time, especially for medium and large-scale problems. A large number of nondominated solutions are achieved within seconds for the first time, for a problem including 720 activities. The proposed Pareto front particle swarm optimizer provides a fast and effective method for optimal scheduling of construction projects. (C) 2016 American Society of Civil Engineers.

Subject Keywords

Scheduling, Costs, Optimization, Algorithms, Multiple objective analysis, Project management

URI

https://hdl.handle.net/11511/33289

Journal

JOURNAL OF COMPUTING IN CIVIL ENGINEERING

DOI

https://doi.org/10.1061/(asce)cp.1943-5487.0000606

Collections

Department of Civil Engineering, Article

Suggestions

OpenMETU
Core

Discrete particle swarm optimization method for the large-scale discrete time-cost trade-off problem Aminbakhsh, Saman; Sönmez, Rifat (2016-06-01) Despite many research studies have concentrated on designing heuristic and meta-heuristic methods for the discrete time-cost trade-off problem (DTCTP), very little success has been achieved in solving large-scale instances. This paper presents a discrete particle swarm optimization (DPSO) to achieve an effective method for the large-scale DTCTP. The proposed DPSO is based on the novel principles for representation, initialization and position-updating of the particles, and brings several benefits for solvin...
Derivative free algorithms for large scale non-smooth optimization and their applications Tor, Ali Hakan; Karasözen, Bülent; Department of Mathematics (2013) In this thesis, various numerical methods are developed to solve nonsmooth and in particular, nonconvex optimization problems. More speciﬁcally, three numerical algorithms are developed for solving nonsmooth convex optimization problems and one algorithm is proposed to solve nonsmooth nonconvex optimization problems. In general, main diﬀerences between algorithms of smooth optimization are in the calculation of search directions, line searches for ﬁnding step-sizes and stopping criteria. However, in nonsmoo...
Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures Hasançebi, Oğuzhan (2008-01-01) In this study the computational performance of adaptive evolution strategies (ESs) in large-scale structural optimization is mainly investigated to achieve the following objectives: (i) to present an ESs based solution algorithm for efficient optimum design of large structural systems consisting of continuous, discrete and mixed design variables; (ii) to integrate new parameters and methodologies into adaptive ESs to improve the computational performance of the algorithm; and (iii) to assess successful self...
Effective optimization with weighted automata on decomposable trees Ravve, E. V.; Volkovich, Z.; Weber, Gerhard Wilhelm (Informa UK Limited, 2014-01-02) In this paper, we consider quantitative optimization problems on decomposable discrete systems. We restrict ourselves to labeled trees as the description of the systems and we use weighted automata on them as our computational model. We introduce a new kind of labeled decomposable trees, sum-like weighted labeled trees, and propose a method, which allows us to reduce the solution of an optimization problem, defined in a fragment of Weighted Monadic Second Order Logic, on such a tree to the solution of effec...
Derivative free optimization of stirrer configurations Schaefer, M.; Karasözen, Bülent; Uğur, Ömür; Yapici, K. (2005-07-01) In the present work a numerical approach for the optimization of stirrer configurations is presented. The methodology is based on a parametrized grid generator, a flow solver, and a mathematical optimization tool, which are integrated into an automated procedure. The grid generator allows the parametrized generation of block-structured grids for the stirrer geometries. The flow solver is based on the discretization of the incompressible Navier-Stokes equations by means of a fully conservative finite-volume ...

Citation Formats

S. Aminbakhsh and R. Sönmez, “Pareto Front Particle Swarm Optimizer for Discrete Time-Cost Trade-Off Problem,” JOURNAL OF COMPUTING IN CIVIL ENGINEERING, pp. 0–0, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33289.