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IEEE Congress on Evolutionary Computation

作者： José L. Rueda István Erlich Department of Electrical Sustainable Energy Delft University of Technology Delft The Netherlands Institute of Electrical Power Systems University Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781479974931

Mean-variance mapping optimization (MVMO) is an emerging evolutionary algorithm, which adopts a single-solution based approach and performs evolutionary operations within a normalized range of the search for all optimization variables. MVMO uses a special mapping function for mutation operation, which allows a controlled shift from exploration priority at early stages of the search process to exploitation at later stages. Recently, the MVMO has been extended to a population-based and hybrid variant denoted as MVMO-SH, which includes strategies for local search and multi-parent crossover. This paper provides an study on the performance of MVMO-SH on the IEEE-CEC 2015 competition test suite on learning-based real-parameter single objective optimization. Experimental results evidence the effectiveness of MVMO-SH for successfully solving different optimization problems with different mathematical properties and dimensionality.

关键词： Heuristic optimization mean-variance mapping optimization single objective optimization learning-based optimization

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Renewable energy system optimization of low/zero energy buildings using single-objective and multi-objective optimization methods

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ENERGY AND BUILDINGS 2015年 89卷 61-75页

作者： Lu, Yuehong Wang, Shengwei Zhao, Yang Yan, Chengchu Hong Kong Polytech Univ Dept Bldg Serv Engn Kowloon Hong Kong Peoples R China

Low energy buildings and zero energy buildings have attracted increasing attention in both academic and professional fields. The performances of these buildings are largely affected by the design of the renewable energy systems. This paper presents a comparison study on two design optimization methods for renewable energy systems in these buildings, including a single objective optimization using Genetic Algorithm and a multi-objectives optimization using Non-dominated Sorting Genetic Algorithm (NSGA-II). Building energy system models and renewable energy system models are developed and adopted, allowing the consideration of the interaction between building energy systems and renewable energy systems in optimization. Two case studies are conducted to evaluate the capability and effectiveness of proposed optimization methods, based on the Hong Kong Zero Carbon Building. The performances of the buildings with the renewable energy systems optimized by both methods are much better than that of the benchmark building in most scenarios. The single objective optimization can provide the "best" solution directly for a given objective while the multi-objective optimization provides rich information for designers to make better compromised decisions. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Low energy building Zero energy building Renewable energy system Genetic Algorithm NSGA-II single objective optimization Multi-objectives optimization

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Multiobjective optimization algorithm for solving constrained single objective problems

Multiobjective optimization algorithm for solving constraine...

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2010 IEEE World Congress on Computational Intelligence

作者： Reynoso-Meza, Gilberto Blasco, Xavier Sanchis, Javier Martinez, Miguel Univ Politecn Valencia Inst Univ Automat & Informat Ind Valencia 46022 Spain

ISBN: (纸本)9781424481262

In this paper, the results for the CEC 2010 Competition and Special Session on Constrained Real-Parameter optimization using the multiobjective differential evolution algorithm with spherical pruning (sp-MODE) are presented. According to the obtained results, the sp-MODE shows to be able to find feasible solutions in highly constrained search spaces.

关键词： Multiobjective optimization single objective optimization constrained optimization spherical pruning technique differential evolution

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MVMO for Bound Constrained single-objective Computationally Expensive Numerical optimization

MVMO for Bound Constrained Single-Objective Computationally ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Rueda, Jose L. Erlich, Istvan Delft Univ Technol Dept Elect Sustainable Energy Delft Netherlands Univ Duisburg Essen Inst Elect Power Syst Duisburg Germany

ISBN: (纸本)9781479974924

Mean-Variance Mapping optimization (MVMO) is a recent addition to the heuristic optimization field. The main traits of its evolutionary mechanism reside in the adoption of a single parent-offspring pair approach along with a normalized range of the search space for all optimization variables as well as in the use of a special mapping function, which accounts for the actual mean and variance of the normalized optimization variables for mutation operation. MVMO is also open to further extensions and hybridization with other approaches. Along this spirit, this paper surveys the performance of MVMO when executed in its pure algorithmic procedure and when hybridized to include a local search strategy. Numerical experiments are conducted on the IEEE-CEC 2015 optimization test bed on bound constrained single-objective computationally expensive numerical optimization. Remarkably, MVMO proves effective at solving different complex problems within a reduced number of allowed function evaluations.

关键词： Computational expensive optimization heuristic optimization mean-variance mapping optimization single objective optimization

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Dynamic Yin-Yang Pair optimization and its Performance on single objective Real Parameter Problems of CEC 2017

Dynamic Yin-Yang Pair Optimization and its Performance on Si...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Maharana, Debasis Kommadath, Remya Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati India

ISBN: (纸本)9781509046010

Yin-Yang Pair optimization is a recently developed metaheuristic technique which searches for the global optima through two stages namely splitting stage and archive stage. A variant of this algorithm is proposed in this work by converting a static archive updating interval to a dynamic one. The performance of this variant is evaluated on the CEC2017 test suite for single objective bound constrained real-parameter numerical optimization problems that is used for the Special Session and Competition of CEC 2017. The results obtained by this algorithm reveals its competitive performance.

关键词： Yin-Yang Pair optimization Dynamic Yin-Yang Pair optimization single objective optimization CEC 2017

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single Phase Multi-Group Teaching Learning Algorithm for single objective Real-Parameter Numerical optimization (CEC2016)

Single Phase Multi-Group Teaching Learning Algorithm for Sin...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Kommadath, Remya Sivadurgaprasad, Chinta Kotecha, Prakash Indian Inst Technol Dept Chem Engn Gauhati India

ISBN: (纸本)9781509006229

In this work, the performance of a variant of Teaching Learning Based optimization algorithm, single Phase Multi-Group Teaching Learning optimization is evaluated on the basis of its performance on single objective real-parameter numerical optimization problems. These problems have been provided as part of one of the competition in IEEE Congress on Evolutionary Computation 2016. It was observed that the performance of the algorithm is competitive to other optimization techniques and satisfactory for many problems.

关键词： single Phase Multi-Group Teaching Learning optimization CEC 2014 single objective optimization stochastic optimization

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Simultaneous Heat Transfer Search for single objective Real-Parameter Numerical optimization Problem

Simultaneous Heat Transfer Search for Single Objective Real-...

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IEEE Region 10 Conference (TENCON)

作者： Maharana, Debasis Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati India

ISBN: (纸本)9781509025978

In this work, we demonstrate the performance of a variant of the Heat Transfer Search algorithm which considers simultaneous heat transfer through all the three modes. It has been demonstrated on the thirty real parameter single objective, bound constrained benchmark functions that have been provided as part of one of the competition in IEEE Congress on Evolutionary Computation 2016. It was observed that the Simultaneous Heat Transfer Search (SHTS) algorithm has a very low time complexity and is competitive to other algorithms in terms of its ability to determine the optimal solutions.

关键词： single objective optimization CEC2016 Competition Simultaneous Heat Transfer Search Computationally Expensive Numerical optimization

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Fog Service Placement optimization: A Survey of State-of-the-Art Strategies and Techniques

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COMPUTERS 2025年第3期14卷 99-99页

作者： Apat, Hemant Kumar Goswami, Veena Sahoo, Bibhudatta Barik, Rabindra K. Saikia, Manob Jyoti Kalinga Inst Ind Technol Sch Comp Applicat Bhubaneswar 751024 India Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela 769008 India Univ Memphis Biomed Sensors & Syst Lab Memphis TN 38152 USA Univ Memphis Elect & Comp Engn Dept Memphis TN 38152 USA

The rapid development of Internet of Things (IoT) devices in various smart city-based applications such as healthcare, traffic management systems, environment sensing systems, and public safety systems produce large volumes of data. To process these data, it requires substantial computing and storage resources for smooth implementation and execution. While centralized cloud computing offers scalability, flexibility, and resource sharing, it faces significant limitations in IoT-based applications, especially in terms of latency, bandwidth, security, and cost. The fog computing paradigm complements the existing cloud computing services at the edge of the network to facilitate the various services without sending the data to a centralized cloud server. By processing the data in fog computing, it satisfies the delay requirement of various time-sensitive services of IoT applications. However, many resource-intensive IoT systems exist that require substantial computing resources for their processing. In such scenarios, finding the optimal computing node for processing and executing the service is a challenge. The optimal placement of various IoT applications services in heterogeneous fog computing environments is a well-known NP-complete problem. To solve this problem, various authors proposed different algorithms like the randomized algorithm, heuristic algorithm, meta heuristic algorithm, machine learning algorithm, and graph-based algorithm for finding the optimal placement. In the present survey, we first describe the fundamental and mathematical aspects of the three-layer IoT-fog-cloud computing model. Then, we classify the IoT application model based on different attributes that help to find the optimal computing node. Furthermore, we discuss the complexity analysis of the service placement problem in detail. Finally, we provide a comprehensive evaluation of both single-objective and multi-objective IoT service placement strategies in fog computing. Additionally, we

关键词： cloud computing fog computing IoT service placement single objective optimization multi-objective optimization

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Simultaneous Heat Transfer Search for single objective Real-Parameter Numerical optimization Problem

Simultaneous Heat Transfer Search for Single Objective Real-...

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IEEE Region 10 Conference

作者： Debasis Maharana Prakash Kotecha Department of Chemical Engineering Indian Institute of Technology Guwahati Department of Chemical Engineering Indian Institute of Technology

ISBN: (纸本)9781509025985

关键词： single objective optimization CEC2016 Competition Simultaneous Heat Transfer Search Computationally Expensive Numerical optimization

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A modified adaptive sparrow search algorithm based on chaotic reverse learning and spiral search for global optimization

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NEURAL COMPUTING & APPLICATIONS 2023年第35期35卷 24603-24620页

作者： Geng, Junqi Sun, Xianming Wang, Haihua Bu, Xianghai Liu, Daohuan Li, Fei Zhao, Zengwu Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Shandong Peoples R China State Grid Shandong Elect Power Co Zibo Power Supp Zibo 255000 Shandong Peoples R China State Grid Shandong Elect Power Co Zouping Power S Binzhou 256600 Shandong Peoples R China Beijing Inst Precis Mechatron & Controls Beijing 100081 Peoples R China

A population-based metaheuristic algorithm that takes its cues from the foraging strategy of sparrows is called the sparrow search algorithm (SSA). While SSA is competitive when compared to other algorithms, it nevertheless has a propensity to carry out imbalanced exploitation and exploration and find the local optimum. Therefore, the modified adaptive sparrow search algorithm (MASSA), an SSA modification, is created to address these problems. To increase population variety, the MASSA uses a chaotic reverse learning technique. Second, to balance the exploitation and exploration capacities, a dynamic adaptive weight is added. In the end, an adaptive spiral search technique improves algorithm performance. Among 23 classical test functions, of which 13 are multidimensional and the other 10 are fixed dimensional, the best chaotic operator is found. It is proven that MASSA is superior. Simulation studies demonstrate that the MASSA described in this study is superior to previous algorithms in terms of stability, convergence speed, and convergence accuracy. Finally, a sample robot path planning problem is resolved using MASSA, and the experimental outcomes confirmed the viability and usefulness of MASSA.

关键词： Metaheuristics Swarm-based algorithm Global optimization problems single objective optimization Sparrow search algorithm (SSA)

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