A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization

Highlights? PBA is based on a particular intelligent behavior of honey bee and bird swarms. ? We compare the performance of PBA with BA and PSO for CSL problems. ? PBA can be efficiently employed to solve CSL problems. The construction site layout (CSL) design presents a particularly interesting area of study because of its relatively high level of attention to usability qualities, in addition to common engineering objectives such as cost and performance. However, it is difficult combinatorial optimization problem for engineers. Swarm intelligence (SI) was very popular and widely used in many complex optimization problems which was collective behavior of social systems such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study proposed an optimization hybrid swarm algorithm namely particle-bee algorithm (PBA) based on a particular intelligent behavior of honey bee and bird swarms by integrates theirs advantages. This study compares the performance of PBA with that of BA and PSO for hypothetical construction engineering of CSL problems. The results show that the performance of PBA is comparable to those of the mentioned algorithms and can be efficiently employed to solve those hypothetical CSL problems with high dimensionality.

[1]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[2]  John S. Gero,et al.  Learning and re-using information in space layout planning problems using genetic engineering , 1997, Artif. Intell. Eng..

[3]  Jeremy J. Michalek,et al.  Architectural layout design optimization , 2002 .

[4]  S. Sahu,et al.  Multiobjective facility layout using simulated annealing , 1993 .

[5]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[6]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[7]  Sue Abdinnour-Helm,et al.  Tabu search based heuristics for multi-floor facility layout , 2000 .

[8]  Hesham Osman,et al.  A hybrid CAD-based construction site layout planning system using genetic algorithms , 2003 .

[9]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

[10]  Tarek Hegazy,et al.  Schedule-dependent evolution of site layout planning , 2001 .

[11]  K. Uosaki,et al.  Improvement of Particle Swarm Optimization for High-Dimensional Space , 2006, 2006 SICE-ICASE International Joint Conference.

[12]  Tarek Hegazy,et al.  A Hybrid AL‐Based System for Site Layout Planning in Construction , 2001 .

[13]  Michael N. Vrahatis,et al.  Parameter selection and adaptation in Unified Particle Swarm Optimization , 2007, Math. Comput. Model..

[14]  Tarek Hegazy,et al.  EvoSite: Evolution-Based Model for Site Layout Planning , 1999 .

[15]  Iris D. Tommelein,et al.  SightPlan Experiments: Alternate Strategies for Site Layout Design , 1991 .

[16]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  Anthony Vannelli,et al.  A New Mathematical-Programming Framework for Facility-Layout Design , 2006, INFORMS J. Comput..

[18]  Xin-She Yang,et al.  Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms , 2005, IWINAC.

[19]  Lale Özbakir,et al.  Bees algorithm for generalized assignment problem , 2010, Appl. Math. Comput..

[20]  Peter E.D. Love,et al.  Genetic search for solving construction site-level unequal-area facility layout problems , 2000 .

[21]  Hsing-Chih Tsai,et al.  Predicting strengths of concrete-type specimens using hybrid multilayer perceptrons with center-unified particle swarm optimization , 2010, Expert Syst. Appl..

[22]  I-Cheng Yeh,et al.  Architectural layout optimization using annealed neural network , 2006 .

[23]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[24]  Ka Chi Lam,et al.  The application of the ant colony optimization algorithm to the construction site layout planning problem , 2007 .