An augmented multi-objective particle swarm optimizer for building cluster operation decisions

Applied Soft Computing Journal

Volumn 25, Issue , 2014, Pages 347-359

  Hu, Mengqi ^a   Weir, Jeffery D ^b   Wu, Teresa ^c  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

^b Air Force Institute of Technology   (United States)

^c ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Multi objective optimization; Pareto optimality; Particle swarm optimization; Smart building; Smart grid

Indexed keywords

INTELLIGENT BUILDINGS; PARTICLE SWARM OPTIMIZATION (PSO); ALGORITHMS; BUILDINGS; CLUSTERING ALGORITHMS; ENERGY EFFICIENCY; EVOLUTIONARY ALGORITHMS; LOCAL AREA NETWORKS; PARETO PRINCIPLE; SMART POWER GRIDS;

MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZER; OPERATION DECISIONS; PARETO-OPTIMALITY; SMART GRID; ENERGY COST SAVINGS; HYPERVOLUME INDICATORS; INTELLIGENT ALGORITHMS; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84908317216     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.08.069     Document Type: Article

References (66)

1. J. Kleissl, and Y. Agarwal Cyber-physical energy systems: focus on smart buildings Proc. 47th Des. Autom. Conf. 2010 749 754
2. T. Hoffmann Smart Buildings 2009 Johnson Controls Inc. Milwaukee, WI
3. J.E. Braun, K.W. Montgomery, and N. Chaturvedi Evaluating the performance of building thermal mass control strategies HVAC&R Res. 7 2001 403 428
4. J.E. Braun Load control using building thermal mass J. Sol. Energy Eng. 125 2003 292 301
5. T.Y. Chen Real-time predictive supervisory operation of building thermal systems with thermal mass Energy Build. 33 2001 141 150
6. N. Etik, N. Allahverdi, I.U. Sert, and I. Saritas Fuzzy expert system design for operating room air-condition control systems Expert Syst. Appl. 36 2009 9753 9758
7. W.-S. Lee, Y. Ting Chen, and T.-H. Wu Optimization for ice-storage air-conditioning system using particle swarm algorithm Appl. Energy 86 2009 1589 1595
8. K.H. Drees, and J.E. Braun Development and evaluation of a rule-based control strategy for ice storage systems HVAC&R Res. 2 1996 312 336
9. S. Liu, and G.P. Henze Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory Part 1: Theoretical foundation Energy Build. 38 2006 142 147
10. S. Liu, and G.P. Henze Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory Part 2: Results and analysis Energy Build. 38 2006 148 161
11. S. Liu, and G.P. Henze Evaluation of reinforcement learning for optimal control of building active and passive thermal storage inventory J. Sol. Energy Eng. 129 2007 215 225
12. A. Rong, and R. Lahdelma An efficient envelope-based branch and bound algorithm for non-convex combined heat and power production planning Eur. J. Oper. Res. 183 2007 412 431
13. A. Rong, H. Hakonen, and R. Lahdelma A variant of the dynamic programming algorithm for unit commitment of combined heat and power systems Eur. J. Oper. Res. 190 2008 741 755
14. S.-E. Fleten, and T.K. Kristoffersen Short-term hydropower production planning by stochastic programming Comput. Oper. Res. 35 2008 2656 2671
15. Y. Wang, J. Zhou, C. Zhou, Y. Wang, H. Qin, and Y. Lu An improved self-adaptive PSO technique for short-term hydrothermal scheduling Expert Syst. Appl. 39 2012 2288 2295
16. G. Mokryani, and P. Siano Optimal wind turbines placement within a distribution market environment Appl. Soft Comput. 13 2013 4038 4046
17. A. Ahmadi, J. Aghaei, H.A. Shayanfar, and A. Rabiee Mixed integer programming of multiobjective hydro-thermal self scheduling Appl. Soft Comput. 12 2012 2137 2146
18. K.K. Mandal, M. Basu, and N. Chakraborty Particle swarm optimization technique based short-term hydrothermal scheduling Appl. Soft Comput. 8 2008 1392 1399
19. M. Sakawa, K. Kato, S. Ushiro, and M. Inaoka Operation planning of district heating and cooling plants using genetic algorithms for mixed integer programming Appl. Soft Comput. 1 2001 139 150
20. K. Deb, F. Ruiz, M. Luque, R. Tewari, J.M. Cabello, and J.M. Cejudo On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization Appl. Soft Comput. 12 2012 3300 3311
21. SIEMENS Smart Grids Make it Possible for Building Operators to Actively Participate in the Electricity Market 2009 Siemens Industry Sector Barcelona, Spain
22. M. Hu, J.D. Weir, and T. Wu Decentralized operation strategies for an integrated building energy system using a memetic algorithm Eur. J. Oper. Res. 217 2012 185 197
23. M. Reyes-Sierra, and C.A. Coello Coello Multi-objective particle swarm optimizers: a survey of the state-of-the-art Int. J. Comput. Intell. Res. 2 2006 287 308
24. S. Boyd EE364b Course Notes: Sub-Gradient Methods, Stanford University 2014 Stanford University available at http://stanford.edu/class/ee364b/lectures.html on March 10
25. J.J. Durillo, J. García-Nieto, A.J. Nebro, C.A. Coello Coello, F. Luna, and E. Alba Multi-objective particle swarm optimizers: an experimental comparison M. Ehrgott, C. Fonseca, X. Gandibleux, J.-K. Hao, M. Sevaux, Evol. Multi-Criterion Optim. 2009 Springer Berlin, Heidelberg 495 509
26. H. Friedman Wiring the Smart Grid for Energy Savings: Integrating Buildings to Maximize Investment 2009 Portland Energy Conservation Inc. (PECI.) Portland, OR
27. J. Kennedy, and R. Eberhart Particle swarm optimization Proc. IEEE Int. Conf. Neural Networks 1995 1995 1942 1948
28. A.P. Engelbrecht Fundamentals of Computational Swarm Intelligence 2006 John Wiley & Sons Hoboken, NJ
29. Y. Shi, and R. Eberhart A modified particle swarm optimizer IEEE Int. Conf. Evol. Comput. Proceedings, 1998. IEEE World Congr. Comput. Intell. 1998 69 73
30. A. Banks, J. Vincent, and C. Anyakoha A review of particle swarm optimization. Part I: Background and development Nat. Comput. 6 2007 467 484
31. X. Li E. Cantú-Paz, J. Foster, K. Deb, L. Davis, R. Roy, U.-M. O'Reilly, A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization 2003 Springer Berlin, Heidelberg 198
32. C.A. Coello Coello, G. Toscano Pulido, and M.S. Lechuga Handling multiple objectives with particle swarm optimization IEEE Trans. Evol. Comput. 8 2004 256 279
33. J.E. Alvarez-Benitez, R.M. Everson, and J.E. Fieldsend A MOPSO algorithm based exclusively on Pareto dominance concepts C. Coello Coello, A. Hernández Aguirre, E. Zitzler, Evol. Multi-Criterion Optim. 2005 Springer Berlin, Heidelberg 459 473
34. Y. Wang, and Y. Yang Particle swarm optimization with preference order ranking for multi-objective optimization Inf. Sci. (NY) 179 2009 1944 1959
35. C.K. Goh, K.C. Tan, D.S. Liu, and S.C. Chiam A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design Eur. J. Oper. Res. 202 2010 42 54
36. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan A fast and elitist multiobjective genetic algorithm: NSGA-II IEEE Trans. Evol. Comput. 6 2002 182 197
37. V.L. Huang, P.N. Suganthan, and J.J. Liang Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems Int. J. Intell. Syst. 21 2006 209 226
38. P.K. Tripathi, S. Bandyopadhyay, and S.K. Pal Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients Inf. Sci. (NY) 177 2007 5033 5049
39. G.G. Yen, and W.F. Leong Dynamic multiple swarms in multiobjective particle swarm optimization IEEE Trans. Syst. Man, Cybern., A: Syst. Humans 39 2009 890 911
40. M. Daneshyari, and G.G. Yen Cultural-based multiobjective particle swarm optimization IEEE Trans. Syst. Man, Cybern., B: Cybern. 41 2011 553 567
41. Z. Michalewicz Genetic Algorithms + Data Structures = Evolution Programs third ed. 1996 Springer-Verlag London, UK
42. M. Hu, T. Wu, and J.D. Weir An intelligent augmentation of particle swarm optimization with multiple adaptive methods Inf. Sci. (NY) 213 2012 68 83
43. V. Plevris, and M. Papadrakakis A hybrid particle swarm-gradient algorithm for global structural optimization Comput. Civ. Infrastruct. Eng. 26 2011 48 68
44. F. Li, T. Wu, and M. Hu Design of a decentralized framework for collaborative product design using memetic algorithms Optim. Eng 2013 10.1007/s11081-012-9210-6 published online January 22
45. I. Das, and J.E. Dennis A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems Struct. Multi. Optim. 14 1997 63 69
46. R.T. Marler, and J.S. Arora Survey of multi-objective optimization methods for engineering Struct. Multi. Optim. 26 2004 369 395
47. X. Zhao Simulated annealing algorithm with adaptive neighborhood Appl. Soft Comput. 11 2011 1827 1836
48. J.C. Spall Multivariate stochastic approximation using a simultaneous perturbation gradient approximation IEEE Trans. Autom. Control 37 1992 332 341
49. S. Mostaghim, and J. Teich Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) Proc. IEEE Swarm Intell. Symp. 2003 2003 26 33
50. M. Laumanns, L. Thiele, K. Deb, and E. Zitzler Combining convergence and diversity in evolutionary multiobjective optimization Evol. Comput. 10 2002 263 282
51. O. Schütze, M. Laumanns, E. Tantar, C.A.C. Coello, and E.-G. Talbi Computing gap free pareto front approximations with stochastic search algorithms Evol. Comput. 18 2010 65 96
52. O. Schütze, M. Laumanns, C. Coello Coello, M. Dellnitz, and E.-G. Talbi Convergence of stochastic search algorithms to finite size pareto set approximations J. Global Optim. 41 2008 559 577
53. S. Mostaghim, and J. Teich The role of e-dominance in multi objective particle swarm optimization methods Congr. Evol. Comput. 2003 Canberra, Australia 2003 1764 1771
54. M. Salazar-Lechuga, and J.E. Rowe Particle swarm optimization and fitness sharing to solve multi-objective optimization problems Congr. Evol. Comput. 2005. 2005 1204 1211
55. C.R. Raquel, and P.C. Naval Jr. An effective use of crowding distance in multiobjective particle swarm optimization Proc. 2005 Conf. Genet. Evol. Comput. 2005 257 264
56. A.J. Nebro, F. Luna, E. Alba, B. Dorronsoro, J.J. Durillo, and A. Beham AbYSS. Adapting scatter search to multiobjective optimization IEEE Trans. Evol. Comput 12 2008 439 457
57. J. Durillo, A. Nebro, F. Luna, and E. Alba Solving three-objective optimization problems using a new hybrid cellular genetic algorithm G. Rudolph, T. Jansen, S. Lucas, C. Poloni, N. Beume, Parallel Probl. Solving from Nat. - PPSN X SE - 66 2008 Springer Berlin, Heidelberg 661 670
58. S.Z. Martínez, and C.A. Coello Coello A multi-objective particle swarm optimizer based on decomposition Proc. 13th Annu. Conf. Genet. Evol. Comput. New York, NY 2011 69 76
59. A.J. Nebro, J.J. Durillo, F. Luna, B. Dorronsoro, and E. Alba MOCell A cellular genetic algorithm for multiobjective optimization Int. J. Intell. Syst. 24 2009 726 746
60. A. Nebro, J. Durillo, M. Machín, C. Coello Coello, and B. Dorronsoro A study of the combination of variation operators in the NSGA-II algorithm C. Bielza, A. Salmerón, A. Alonso-Betanzos, J.I. Hidalgo, L. Martínez, A. Troncoso, Adv. Artif. Intell. SE - 28 2013 Springer Berlin, Heidelberg 269 278
61. M. Reyes-Sierra, and C.A. Coello Coello Improving PSO-based multi-objective optimization using crowding, mutation and -dominance C. Coello Coello, A. Hernández Aguirre, E. Zitzler, Evol. Multi-Criterion Optim. 2005 Springer Berlin, Heidelberg 505 519
62. E. Zitzler, M. Laumanns, and L. Thiele SPEA2. Improving the strength pareto evolutionary algorithm for multiobjective optimization Evol. Methods Des. Optim. Control with Appl. to Ind. Probl 2001 95 100
63. J.J. Durillo, and A.J. Nebro jMetal: a Java framework for multi-objective optimization Adv. Eng. Softw. 42 2011 760 771
64. S. García, D. Molina, M. Lozano, and F. Herrera A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 special session on real parameter optimization J. Heuristics 15 2009 617 644
65. Q. Zhang, and H. Li MOEA/D. A multiobjective evolutionary algorithm based on decomposition IEEE Trans. Evol. Comput. 11 2007 712 731
66. J. Carpenter, P. Clifford, and P. Fearnhead Improved particle filter for nonlinear problems IEE Proc. Radar Sonar Navig. 146 1999 2 7