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Optimal chiller loading in HVAC System Using a Novel algorithm Based on the distributed framework

JOURNAL OF BUILDING ENGINEERING 2020年 28卷

作者： Yu, Junqi Liu, Qite Zhao, Anjun Qian, Xuegen Zhang, Rui Xian Univ Architecture & Technol Xian Shaanxi Peoples R China

Aimed at the optimal chiller loading (OCL) problem in heating, ventilation and air conditioning (HVAC) system, a distributed framework for HVAC control is introduced and discussed. And furthermore, the distributed chaotic estimation of distribution algorithm (DCEDA) based on this framework is proposed. Firstly, compared with the centralized framework, the distributed framework has the features of flexibility and expansibility. Therefore, the distributed framework can fit the development of HVAC control system better. Secondly, in the proposed algorithm, an initialization methodology based on logistic map is developed and the chaotic mutation mechanism is applied to increase the search capability of the algorithm. To testify the performance of DCEDA, two well-known cases based on the OCL problem are tested and the results are compared with other algorithms. The results show that DCEDA is an efficient distributed optimization algorithm with good robustness, stability and convergence, and it can achieve significant energy saving effect.

关键词： Optimal chiller loading Distributed framework estimation of distribution algorithm Energy saving

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A meta-heuristic for topology optimization using probabilistic learning

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APPLIED INTELLIGENCE 2018年第11期48卷 4267-4287页

作者： Ivvan Valdez, S. Marroquin, Jose L. Botello, Salvador Faurrieta, Noe Univ Guanajuato Div Engn Guanajuato 36885 Mexico CIMAT Ctr Res Mathemat AC Jalisco S-N Guanajuato 36000 Mexico

Topological shape optimization consists in finding the optimal shape of a mechanical structure by means of a process for removing or inserting new holes and structural elements, that is to say, using a process which produces topological changes. This article introduces a method for automated topological optimization via an estimation of distribution algorithm (EDA), a global optimization meta-heuristic based on probabilistic learning, which requires of neither user initialization, nor a priori information or design bias in the algorithm. We propose a representation and solution mapping which favors feasible structures and requires a, relatively, low dimensionality (some hundreds), the probabilistic model learns from finite element evaluations to generate well-performed structures. The EDA for topology optimization (EDATOP) is compared with an algorithm, in the state of the art, specially designed to address this problem, demonstrating that our approach is useful for solving real-world problems, escapes from local optima, and delivers better solutions than the comparing algorithm which uses problem knowledge, with a payoff on the computational cost.

关键词： Topological optimization Probabilistic learning Global optimization estimation of distribution algorithm Stress constraint

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Information fusion in offspring generation: A case study in DE and EDA

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 42卷 99-108页

作者： Fang, Hui Zhou, Aimin Zhang, Hu East China Normal Univ Dept Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai 200062 Peoples R China Beijing Electromech Engn Inst Beijing 100074 Peoples R China

Both differential evolution (DE) and estimation of distribution algorithm (EDA) are popular and effective evolutionary algorithms (EM) in solving global optimization problems. The two algorithms utilize different kinds of information for generating offspring solutions. In the former, the mutation and crossover operators use the individual information to create trial solutions, while in the later, a probabilistic model is built for sampling new trial solutions, which extracts the population distribution information. It is therefore natural to make use of both kinds of information for generating solutions. In this paper, we propose an algorithm that hybridizes DE and EDA, named as DE/GM, which utilizes both DE crossover/mutation operators and a Gaussian probabilistic model based operator for offspring generation. The basic idea is to generate some of trial solutions by the EDA operator, and to generate the rest by the DE operator. To validate the performance of DE/GM, a test suite of 13 benchmark functions is employed, and the experimental results suggest that DE/GM is promising.

关键词： Differential evolution estimation of distribution algorithm Hybrid algorithm Information fusion

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A compact genetic algorithm for the network coding based resource minimization problem

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APPLIED INTELLIGENCE 2012年第4期36卷 809-823页

作者： Xing, Huanlai Qu, Rong Univ Nottingham Sch Comp Sci Automated Scheduling Optimisat & Planning ASAP Gr Nottingham NG8 1BB England

In network coding based data transmission, intermediate nodes in the network are allowed to perform mathematical operations to recombine (code) data packets received from different incoming links. Such coding operations incur additional computational overhead and consume public resources such as buffering and computational resource within the network. Therefore, the amount of coding operations is expected to be minimized so that more public resources are left for other network applications. In this paper, we investigate the newly emerged problem of minimizing the amount of coding operations required in network coding based multicast. To this end, we develop the first elitism-based compact genetic algorithm (cGA) to the problem concerned, with three extensions to improve the algorithm performance. First, we make use of an all-one vector to guide the probability vector (PV) in cGA towards feasible individuals. Second, we embed a PV restart scheme into the cGA where the PV is reset to a previously recorded value when no improvement can be obtained within a given number of consecutive generations. Third, we design a problem-specific local search operator that improves each feasible solution obtained by the cGA. Experimental results demonstrate that all the adopted improvement schemes contribute to an enhanced performance of our cGA. In addition, the proposed cGA is superior to some existing evolutionary algorithms in terms of both exploration and exploitation simultaneously in reduced computational time.

关键词： Compact genetic algorithm estimation of distribution algorithm Multicast Network coding

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An evolutionary fuzzy scheduler for multi-objective resource allocation in fog computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 117卷 498-509页

作者： Wu, Chu-ge Li, Wei Wang, Ling Zomaya, Albert Y. Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Sydney Ctr Distributed & High Performance Comp Sch Comp Sci Sydney NSW 2006 Australia

With rapid development of the Internet of Things (IoT), a vast amount of raw data produced by IoT devices needs to be processed promptly. Compared to cloud computing, fog computing nodes are closer to data resource for decreasing the end-to-end transmission latency. Considering the limited resource of IoT devices, offloading computationally-intensive tasks to the servers with high computing capability is essential in the IoT-fog-cloud system to complete those tasks on time. In this work, we propose a fuzzy logical offloading strategy for IoT applications characterized by uncertain parameters to optimize both agreement index and robustness. A multi-objective estimation of distribution algorithm (EDA) is designed to learn and optimize the fuzzy offloading strategy from a diversity of the applications. The algorithm partitions applications into independent clusters, so that each cluster can be allocated to the corresponding tier for further processing. Thus, system resources are saved by making scheduling decisions in a reduced search space. Simulation studies on benchmark problems and real-world cases are carried out to verify the efficiency of our proposed algorithm. Pareto sets produced by our algorithm outperformed classic heuristic solutions for 88.3% benchmark cases and dominated Pareto sets of two state-of-art multi-objective algorithms for 92.7% and 94.4% cases correspondingly. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Edge computing Internet of Things Evolutionary computation estimation of distribution algorithm Fuzzy scheduling Agreement index Robustness

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Probabilistic distribution learning algorithm based transmit antenna selection and precoding for millimeter wave massive MIMO systems

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TELECOMMUNICATION SYSTEMS 2021年第3期76卷 449-460页

作者： Khalid, Salman Mehmood, Rashid bin Abbas, Waqas Khalid, Farhan Naeem, Muhammad Natl Univ Comp & Emerging Sci NUCES Islamabad Pakistan COMSATS Univ Wah Campus Islamabad Pakistan

In modern day communication systems, the massive MIMO architecture plays a pivotal role in enhancing the spatial multiplexing gain, but vice versa the system energy efficiency is compromised. Consequently, resource allocation in-terms of antenna selection becomes inevitable to increase energy efficiency without having any obvious effect or compromising the system spectral efficiency. Optimal antenna selection can be performed using exhaustive search. However, for a massive MIMO architecture, exhaustive search is not a feasible option due to the exponential growth in computational complexity with an increase in the number of antennas. We have proposed a computationally efficient and optimum algorithm based on the probability distribution learning for transmit antenna selection. An estimation of the distribution algorithm is a learning algorithm which learns from the probability distribution of best possible solutions. The proposed solution is computationally efficient and can obtain an optimum solution for the real time antenna selection problem. Since precoding and beamforming are also considered essential techniques to combat path loss incurred due to high frequency communications, so after antenna selection, successive interference cancellation algorithm is adopted for precoding with selected antennas. Simulation results verify that the proposed joint antenna selection and precoding solution is computationally efficient and near optimal in terms of spectral efficiency with respect to exhaustive search scheme. Furthermore, the energy efficiency of the system is also optimized by the proposed algorithm, resulting in performance enhancement of massive MIMO systems.

关键词： Millimeter wave propagation Hybrid (analog and digital)precoding estimation of distribution algorithm Spectral efficiency Exhaustive search Random search Transmit antenna selection

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Efficient model building in competent genetic algorithms using DSM clustering

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AI COMMUNICATIONS 2011年第3期24卷 213-231页

作者： Nikanjam, Amin Sharifi, Hadi Rahmani, Adel T. Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran

Detecting multivariate interactions between the variables of a problem is a challenge in traditional genetic algorithms (GAs). This issue has been addressed in the literature as the linkage learning problem. It is widely acknowledged that the success of GA in solving any problem depends on the proper detection of multivariate interactions in the problem. Different approaches have thus been proposed to detect and represent such interactions. estimation of distribution algorithms (EDAs) are amongst these approaches that have been successfully applied to a wide range of hard optimization problems. They build a model of the problem to detect multivariate interactions, but the model building process is often computationally intensive. In this paper, we propose a new clustering algorithm that turns pair-wise interactions in a dependency structure matrix (DSM) into an interaction model efficiently. The model building process is carried out before the evolutionary algorithm to save computational burden. The accurate interaction model obtained in this way is then used to perform an effective recombination of building blocks (BBs) in the GA. We applied the proposed approach to solve exemplar hard optimization problems with different types of linkages to show the effectiveness and efficiency of the proposed approach. Theoretical analysis and experiments showed that the building of an accurate model requires O(n log(n)) number of fitness evaluations. The comparison of the proposed approach with some existing algorithms revealed that the efficiency of the model building process is enhanced significantly.

关键词： estimation of distribution algorithm model building linkage learning dependency structure matrix

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Linkage learning by number of function evaluations estimation: Practical view of building blocks

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INFORMATION SCIENCES 2013年 230卷 162-182页

作者： Fan, Kai-Chun Yu, Tian-Li Lee, Jui-Ting Natl Taiwan Univ Dept Elect Engn Taiwan Evolutionary Intelligence Lab Taipei Taiwan

estimation of distribution algorithms (EDAs) identify linkages among genes and build models which decompose a given problem. EDAs have been successfully applied to many real-world problems;however, whether their models indicate the optimal way to decompose the given problem is rarely studied. This paper proposes using the number of function evaluations (N-fe) as the performance measure of EDA models. As a result, the optimal model can be defined as the one that consumes the fewest N-fe on average for EDAs to solve a specific problem. Based on this concept, correct building blocks (BBs) can be defined as groups of genes that construct the optimal model. Similarly, linkages within a BB are defined as the correct linkages of which the specific problem consists. The capabilities of four commonly used linkage-learning metrics, nonlinearity, entropy, simultaneity and differential mutual complement, are investigated based on the above definitions. For certain partially separable problems, none of the above metrics yields difference that is statistically significant between linear and nonlinear gene pairs. Although an optimal threshold still exists to separate linear and nonlinear gene pairs, most existing EDA designs today have not yet characterize such threshold. Based on the idea of N-fe estimation, this paper also proposes a metric enhancer, named eNFE, to enhance existing linkage-learning techniques. Empirical results show that eNFE improves BB identification by eliminating spurious linkages which occur often in most existing EDAs. (c) 2013 Elsevier Inc. All rights reserved.

关键词： estimation of distribution algorithm Linkage learning Building block Genetic algorithm Number of function evaluations

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Interval-based ranking in noisy evolutionary multi-objective optimization

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2015年第2期61卷 517-555页

作者： Karshenas, Hossein Bielza, Concha Larraaga, Pedro Univ Politecn Madrid Fac Informat Computat Intelligence Grp E-28660 Madrid Spain

As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many real-world multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present -degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standard multi-objective evolutionary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experimental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization.

关键词： Noisy optimization Interval analysis Evolutionary algorithms Multi-objective optimization Joint probabilistic modeling estimation of distribution algorithm

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Precision Analysis for an Optimal Parallel IIR Filter's Implementation

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2022年第8期41卷 4512-4546页

作者： Zelmat, Mohammed Lamini, El-Sedik Tagzout, Samir Belbachir, Hacene Belouchrani, Adel USTHB RECITS Lab Dept Operat Res Fac Math Algiers Algeria Res Ctr Appl Econ Dev Algiers Algeria EPE PROXYLAN SPA Lot 1Route Fouka Kolea Tipaza Algeria CERIST Res Ctr Sci & Tech Informat Algiers Algeria Ecole Natl Polytech LDCCP Lab Dept Elect Engn Algiers Algeria

This paper addresses the precision analysis of filters in the parallel form framework. The precision analysis consists of determining suitable fractional bit-widths to set a tradeoff between resource consumption and computational accuracy. Although the stability of the parallel form is relatively better controlled than the related direct form, its bit-width optimization did not receive much consideration in the literature, despite its significant contribution to the optimization of the filter's physical implementation. To carry out the needed bit-width optimization, we present two heuristics based on the estimation of distribution algorithm, which falls within the category of probabilistic model-building genetic algorithm. The performance of the proposed approach is discussed, and compared to chosen benchmarks, the results show that our hardware implementations reduce the cost of the resulted circuits up to 37%.

关键词： Bit-width optimization estimation of distribution algorithm IIR filter Parallel form Precision analysis

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