Power Consumption Optimization of a Building Using Multiobjective Particle Swarm Optimization

2015 ◽  
Vol 72 (2) ◽  
Author(s):  
Ahmad Faiz Ab Rahman ◽  
Hazlina Selamat ◽  
Fatimah Sham Ismail ◽  
Nurulaqilla Khamis
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Low Cost ◽  
Particle Swarm ◽  
Sorting Algorithm ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Building Energy Optimization ◽  
Save Energy

This paper discusses the development of a building energy optimization algorithm by using multiobjective Particle Swarm Optimization for a building. Particle Swarm Optimization is a well known algorithm that is proven to be effective in many complex optimization problems. Multiobjective PSO is developed by utilizing non-dominated sorting algorithm in tandem with majority-based selection algorithm. The optimizer is written by using MATLAB alongside its GUI interface. Results are then analyzed by using the Binh and Korn benchmark test and natural distance performance metrics. From the results, the optimizer is capable to minimize up to 42 percent of energy consumption and lowering the electricity bills up to 43 percent, while still maintaining comfort at more than 95 percent as well. With this, building owner can save energy with a low-cost and simple solution. 

scholarly journals Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Wusi Yang ◽  
Li Chen ◽  
Yi Wang ◽  
Maosheng Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Competition Mechanism ◽  
Multiobjective Particle Swarm Optimization

The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.

scholarly journals A Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Hongli Yu ◽  
Yuelin Gao ◽  
Jincheng Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Distance Method ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Mutation Strategy ◽  
Speed Up ◽  
Particle Parameters

In order to solve the shortcomings of particle swarm optimization (PSO) in solving multiobjective optimization problems, an improved multiobjective particle swarm optimization (IMOPSO) algorithm is proposed. In this study, the competitive strategy was introduced into the construction process of Pareto external archives to speed up the search process of nondominated solutions, thereby increasing the speed of the establishment of Pareto external archives. In addition, the descending order of crowding distance method is used to limit the size of external archives and dynamically adjust particle parameters; in order to solve the problem of insufficient population diversity in the later stage of algorithm iteration, time-varying Gaussian mutation strategy is used to mutate the particles in external archives to improve diversity. The simulation experiment results show that the improved algorithm has better convergence and stability than the other compared algorithms.

scholarly journals Dynamic Shannon Performance in a Multiobjective Particle Swarm Optimization

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 827 ◽  
Author(s):  
E. J. Solteiro Pires ◽  
J. A. Tenreiro Machado ◽  
P. B. de Moura Oliveira
Keyword(s):  
Particle Swarm Optimization ◽  
Collective Behavior ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Multiobjective Particle Swarm Optimization ◽  
Multiobjective Algorithms ◽  
Average Fitness

Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms.

scholarly journals An Improved Multiobjective Quantum-Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Fei Han ◽  
Yu-Wen-Tian Sun ◽  
Qing-Hua Ling
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Search Strategy ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Pattern ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Multiobjective Particle Swarm Optimization ◽  
Convergence Performance

Although multiobjective particle swarm optimization (MOPSO) has good performance in solving multiobjective optimization problems, how to obtain more accurate solutions as well as improve the distribution of the solutions set is still a challenge. In this paper, to improve the convergence performance of MOPSO, an improved multiobjective quantum-behaved particle swarm optimization based on double search strategy and circular transposon mechanism (MOQPSO-DSCT) is proposed. On one hand, to solve the problem of the dramatic diversity reduction of the solutions set in later iterations due to the single search pattern used in quantum-behaved particle swarm optimization (QPSO), the double search strategy is proposed in MOQPSO-DSCT. The particles mainly learn from their personal best position in earlier iterations and then the particles mainly learn from the global best position in later iterations to balance the exploration and exploitation ability of the swarm. Moreover, to alleviate the problem of the swarm converging to local minima during the local search, an improved attractor construction mechanism based on opposition-based learning is introduced to further search a better position locally as a new attractor for each particle. On the other hand, to improve the accuracy of the solutions set, the circular transposon mechanism is introduced into the external archive to improve the communication ability of the particles, which could guide the population toward the true Pareto front (PF). The proposed algorithm could generate a set of more accurate and well-distributed solutions compared to the traditional MOPSO. Finally, the experiments on a set of benchmark test functions have verified that the proposed algorithm has better convergence performance than some state-of-the-art multiobjective optimization algorithms.

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

scholarly journals Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ya-zhong Luo ◽  
Li-ni Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Mission Design ◽  
Trajectory Design ◽  
Time Transfer ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

scholarly journals Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on sorting and OneMax

2021 ◽  
Author(s):  
Moritz Mühlenthaler ◽  
Alexander Raß ◽  
Manuel Schmitt ◽  
Rolf Wanka
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Chain ◽  
Markov Model ◽  
Optimization Problems ◽  
Formal Analysis ◽  
Particle Swarm ◽  
Upper And Lower Bounds ◽  
Swarm Optimization ◽  
Local Optima ◽  
Sorting Problem

AbstractMeta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. We provide a comprehensive formal analysis of the performance of this algorithm on certain “easy” reference problems in a black-box setting, namely the sorting problem and the problem OneMax. In our analysis we use a Markov model of the proposed algorithm to obtain upper and lower bounds on its expected optimization time. Our bounds are essentially tight with respect to the Markov model. We show that for a suitable choice of algorithm parameters the expected optimization time is comparable to that of known algorithms and, furthermore, for other parameter regimes, the algorithm behaves less greedy and more explorative, which can be desirable in practice in order to escape local optima. Our analysis provides a precise insight on the tradeoff between optimization time and exploration. To obtain our results we introduce the notion of indistinguishability of states of a Markov chain and provide bounds on the solution of a recurrence equation with non-constant coefficients by integration.

scholarly journals Chunking and cooperation in particle swarm optimization for feature selection

2021 ◽  
Author(s):  
Malek Sarhani ◽  
Stefan Voß
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Cooperative Learning ◽  
Optimization Problems ◽  
Practical Importance ◽  
Particle Swarm ◽  
Large Datasets ◽  
Social Phenomena ◽  
Swarm Optimization ◽  
Complex Optimization

AbstractBio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

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