Micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO) of a solar thermal combisystem

Abstract Multi-objective optimization (MOO) algorithms usually require a high number of objective function evaluations to approximate the Pareto-optimal solutions, which can be time-consuming in many engineering applications. To overcome this issue, MOO algorithms using a small population, often referred to as micro-MOO algorithms, aim at approximating the true Pareto front using a smaller number of objective function evaluations. Such algorithms are not common in the evolutionary algorithms literature and their usage in building engineering is seldom. This paper proposes a micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO), which is a revised version of the micro-MOPSO algorithm. First, the proposed algorithm is applied along with eight other MOO algorithms to 24 benchmark problems, and their performance is compared by using two metrics. Although the proposed micro-TVMOPSO algorithm faced difficulties in solving some complicated Pareto fronts, it outperformed the eight optimization algorithms on 10 of the 24 selected benchmark problems. After the comparison of proposed micro-TVMOPSO with several MOO algorithms for different benchmark problems, the micro-TVMOPSO is applied to a case study in engineering: the design optimization of a residential solar thermal combisystem using two conflicting objective functions, the life cycle cost (LCC) and energy use (LCE). Different patterns of variation of the decision variables were observed from the non-dominated solutions found by micro-TVMOPSO, which would have not been possible to notice by performing a single-objective optimization. For instance, the increase of number of solar collectors from 1 to 10 had the impact of increasing the LCC by 84% and decreasing the LCE by 63%. The results indicated that the number of solar thermal collectors is the variable having the most effect on both LCC and LCE. When the number of collectors increases, more energy is harvested, and larger tanks and less auxiliary electric power are needed.

[1]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[2]  Carlos A. Coello Coello,et al.  Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer That Uses a Very Small Population Size , 2010, Multi-Objective Swarm Intelligent System.

[3]  Radu Zmeureanu,et al.  Multi-objective optimization of a residential solar thermal combisystem , 2016 .

[4]  R. Lyndon While,et al.  A review of multiobjective test problems and a scalable test problem toolkit , 2006, IEEE Transactions on Evolutionary Computation.

[5]  Enrique Alba,et al.  AbYSS: Adapting Scatter Search to Multiobjective Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[6]  Hong Li,et al.  MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives , 2013, Comput. Oper. Res..

[7]  Wang Hu,et al.  Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate System , 2015, IEEE Transactions on Evolutionary Computation.

[8]  Shengxiang Yang,et al.  Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization , 2014, IEEE Transactions on Cybernetics.

[9]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[10]  Konstantinos Liagkouras,et al.  Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review , 2012, Expert Syst. Appl..

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[13]  Radu Zmeureanu,et al.  Solar combisystem with seasonal thermal storage , 2010 .

[14]  Bernhard Sendhoff,et al.  Comparative studies on micro heat exchanger optimisation , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[15]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

[16]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[17]  Peter J. Fleming,et al.  On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers , 1996, PPSN.

[18]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[19]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[20]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[21]  Qingfu Zhang,et al.  Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition , 2009 .

[22]  Raffaele Bornatico,et al.  Optimal sizing of a solar thermal building installation using particle swarm optimization , 2012 .

[23]  Christian Fonteix,et al.  Multicriteria optimization using a genetic algorithm for determining a Pareto set , 1996, Int. J. Syst. Sci..

[24]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[25]  Radu Zmeureanu,et al.  Life cycle cost and energy analysis of a Net Zero Energy House with solar combisystem , 2011 .

[26]  Carlos A. Coello Coello,et al.  Multi-Objective Particle Swarm Optimizers: An Experimental Comparison , 2009, EMO.

[27]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[28]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[29]  Min Huang,et al.  Multiobjective optimisation design for enterprise system operation in the case of scheduling problem with deteriorating jobs , 2016, Enterp. Inf. Syst..

[30]  Lothar Thiele,et al.  On Set-Based Multiobjective Optimization , 2010, IEEE Transactions on Evolutionary Computation.

[31]  Bo Zhang,et al.  Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers , 2016, IEEE Transactions on Evolutionary Computation.

[32]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[33]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, ANTS Conference.

[34]  Jie Zhang,et al.  Consistencies and Contradictions of Performance Metrics in Multiobjective Optimization , 2014, IEEE Transactions on Cybernetics.

[35]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[36]  Jonathan A. Wright,et al.  A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization , 2004 .

[37]  Carlos A. Coello Coello,et al.  Handling Constraints in Particle Swarm Optimization Using a Small Population Size , 2007, MICAI.

[38]  Crump Sl,et al.  The present status of variance component analysis. , 1951 .

[39]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach , 2014, IEEE Transactions on Evolutionary Computation.

[40]  C. Coello,et al.  Multiobjective optimization using a micro-genetic algorithm , 2001 .

[41]  Carlos A. Coello Coello,et al.  A Study of the Parallelization of a Coevolutionary Multi-objective Evolutionary Algorithm , 2004, MICAI.

[42]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[43]  Faouzi Masmoudi,et al.  A Sensitivity Analysis of Multi-objective Cooperative Planning Optimization Using NSGA-II , 2014 .

[44]  Carlos A. Coello Coello,et al.  A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques , 1999, Knowledge and Information Systems.

[45]  Ye Tian,et al.  A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[46]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[47]  Hong Li,et al.  A modification to MOEA/D-DE for multiobjective optimization problems with complicated Pareto sets , 2012, Inf. Sci..

[48]  Bahriye Akay,et al.  Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms , 2012, Journal of Global Optimization.

[49]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .

[50]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[51]  Christophe Ley,et al.  Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median , 2013 .

[52]  Ala Hasan,et al.  Minimisation of life cycle cost of a detached house using combined simulation and optimisation , 2008 .

[53]  Shengxiang Yang,et al.  A Grid-Based Evolutionary Algorithm for Many-Objective Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[54]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[55]  Carlos A. Coello Coello,et al.  The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization , 2003, EMO.

[56]  Peter Kulchyski and , 2015 .

[57]  Damiano Pasini,et al.  A non-dominated sorting hybrid algorithm for multi-objective optimization of engineering problems , 2011 .

[58]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[59]  R. Zmeureanu,et al.  Optimization of a residential solar combisystem for minimum life cycle cost, energy use and exergy destroyed , 2014 .

[60]  Enrique Alba,et al.  SMPSO: A new PSO-based metaheuristic for multi-objective optimization , 2009, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM).

[61]  Antonio J. Nebro,et al.  jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..

[62]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.