NIDE: A Novel Improved Differential Evolution for Construction Project Crashing Optimization

In the field of construction management, project crashing is an approach to shortening the project duration by reducing the duration of several critical project activities to less than their normal activity duration. The goal of crashing is to shorten the project duration while minimizing the crashing cost. In this research, a novel method for construction project crashing is proposed. The method is named as novel improved differential evolution (NIDE). The proposed NIDE is developed by an integration of the differential evolution (DE) and a new probabilistic similarity-based selection operator (PSSO) that aims at improving the DE’s selection process. The PSSO has the role as a scheme for preserving the population diversity and fending off the premature convergence. The experimental result has demonstrated that the newly established NIDE can successfully escape from local optima and achieve a significantly better optimization performance.

[1]  Rammohan Mallipeddi.  Harmony Search Based Parameter Ensemble Adaptation for Differential Evolution , 2013, J. Appl. Math..

[2]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[3]  Min-Yuan Cheng,et al.  Groutability Estimation of Grouting Processes with Microfine Cements Using an Evolutionary Instance-Based Learning Approach , 2014, J. Comput. Civ. Eng..

[4]  I-Tung Yang,et al.  Performing complex project crashing analysis with aid of particle swarm optimization algorithm , 2007 .

[5]  Nhat-Duc Hoang,et al.  An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine , 2014 .

[6]  Glenn A. Sears,et al.  Construction project management : a practical guide to field construction management , 2008 .

[7]  Min-Yuan Cheng,et al.  Risk Score Inference for Bridge Maintenance Project Using Evolutionary Fuzzy Least Squares Support Vector Machine , 2014, J. Comput. Civ. Eng..

[8]  Wenjian Luo,et al.  Differential evolution with dynamic stochastic selection for constrained optimization , 2008, Inf. Sci..

[9]  Antonin Ponsich,et al.  Differential Evolution performances for the solution of mixed-integer constrained process engineering problems , 2011, Appl. Soft Comput..

[10]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[11]  Min-Yuan Cheng,et al.  Hybrid intelligence approach based on LS-SVM and Differential Evolution for construction cost index estimation: A Taiwan case study , 2013 .

[12]  Arthur C. Sanderson,et al.  JADE: Self-adaptive differential evolution with fast and reliable convergence performance , 2007, 2007 IEEE Congress on Evolutionary Computation.

[13]  H. N. Ahuja,et al.  Project management : techniques in planning and controlling construction projects / Hira N. Ahuja, S.P. Dozzi, S.M. Aourizk , 1994 .

[14]  C. Coello,et al.  Cultured differential evolution for constrained optimization , 2006 .

[15]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[16]  Stephan Foldes,et al.  PERT and crashing revisited: Mathematical generalizations , 1990 .

[17]  Nhat-Duc Hoang,et al.  A Novel Resource-Leveling Approach for Construction Project Based on Differential Evolution , 2014 .

[18]  Swagatam Das,et al.  Automatic Clustering Using an Improved Differential Evolution Algorithm , 2007 .

[19]  Prabuddha De,et al.  The discrete time-cost tradeoff problem revisited , 1995 .

[20]  Saleh Mubarak.  Construction Project Scheduling and Control: Mubarak/Construction Project Scheduling 2E , 2010 .

[21]  W. Verdini,et al.  Nonlinear time/cost tradeoff models in project management , 1995 .

[22]  Ren-Jye Dzeng.  Identifying a design management package to support concurrent design in building wafer fabrication facilities , 2006 .

[23]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[24]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[25]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[26]  Min-Yuan Cheng,et al.  A novel time-depended evolutionary fuzzy SVM inference model for estimating construction project at completion , 2012, Eng. Appl. Artif. Intell..

[27]  J. Horowitz,et al.  Critical Path Problems with Concave Cost-Time Curves , 1972 .

[28]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[29]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[30]  Min-Yuan Cheng,et al.  A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization , 2012, Expert Syst. Appl..

[31]  Carlos A. Coello Coello,et al.  Simple Feasibility Rules and Differential Evolution for Constrained Optimization , 2004, MICAI.