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An Indicator-Based Evolutionary Algorithm for Large-Scale Constrained Multi-objective optimization

IEEE Transactions on Evolutionary Computation 2025年

作者： Zhong, Xiaoyu Yao, Xiangjuan Qiao, Kangjia Gong, Dunwei Jin, Yaochu China University of Mining and Technology School of Mathematics Xuzhou221116 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao266061 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Westlake University School of Engineering Hangzhou310030 China

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) experience a dramatic performance degradation when solving large-scale constrained multi-objective optimization problems (LSCMOPs), since they converge very slowly and easily get trapped in local optima due to the loss of diversity. To enhance the efficiency of tackling LSCMOPs, this paper proposes an indicator-based evolutionary algorithm, referred to as ILCMO. In ILCMO, two complementary indicators are proposed to assess the contribution of each individual to feasibility, convergence, and diversity. The first is a feasibility-oriented indicator designed to drive the population towards the feasible regions. The second is an infeasibility-assisted dynamic indicator, which comprises two relaxed constraint boundaries. Theoretical studies demonstrate that this dynamic indicator can effectively guide the population to focus on evenly searching the infeasible regions around feasible solutions to enhance local diversity. In addition, a variable grouping-based differential evolution (VGDE) strategy, which includes a group-based intra-learning operator and a group-based inter-learning operator, is devised to improve the quality of reproduction in large-scale search spaces. The effectiveness of the proposed algorithm is validated through comprehensive experiments on four benchmarks and a microgrid dispatch problem against seven state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： optimization algorithms

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Next-Generation Edge Computing Assisted Autonomous Driving Based Artificial Intelligence algorithms

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IEEE ACCESS 2022年 10卷 53987-54001页

作者： Ibn-Khedher, Hatem Laroui, Mohammed Moungla, Hassine Afifi, Hossam Abd-Elrahman, Emad Capgemini Engn F-75017 Paris France Univ Djillali Liabes Sidi Bel Abbes Comp Sci Dept Sidi Bel Abbes 22000 Algeria Univ Paris Cite Lab Informat Paris Descartes LIPADE F-75006 Paris France Inst Polytech Paris Telecom SudParis F-91764 Palaiseau France Natl Telecommun Inst NTI Cairo 11768 Egypt

Edge Computing and Network Function Virtualization (NFV) concepts can improve network processing and multi-resources allocation when intelligent optimization algorithms are deployed. Multiservice offloading and allocation approaches pose interesting challenges in the current and next-generation vehicle networks. The state-of-the-art optimization approaches still formulate exact algorithms, and tune approximation methods to get sufficient solutions. These approaches are data-centric that aim to use heterogeneous data inputs to find the near optimal solutions. In the context of connected and autonomous vehicles (CAVs), these techniques show an exponential computational time and deal only with small and medium scale networks. Therefore, we are motivated by using recent Deep Reinforcement Learning (DRL) techniques to learn the behavior of exact optimization algorithms while enhancing the Quality of Service (QoS) of network operators and satisfying the requirements of the next-generation Autonomous Vehicles (AVs). DRL algorithms can improve AVs service offloading and optimize edge resources. An Optimal Virtual Edge Autopilot Placement (OVEAP) algorithm is proposed using Integer Linear Programming (ILP). Moreover, an autopilot placement protocol is presented to support the algorithm. Optimal allocation and Virtual Network Function (VNF) placement and chaining of the autopilot, based on several new constraints such as computing and networking loads, network edge infrastructure, and placement cost, are designed. Further, a DRL approach is formulated to deal with dense Internet of Autonomous Vehicle (IoAV) networks. Extensive simulations and evaluations are carried out. Results show that the proposed allocation strategies outperform the state-of-the-art solutions and give better performance in terms of Total Edge Servers Utilization, Total Edge Servers Allocation Time, and Successfully Allocated autopilots.

关键词： Autonomous vehicles Autopilot Servers Sensors Resource management Artificial intelligence optimization Edge computing artificial intelligence autonomous driving optimization algorithms

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Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第11期36卷 3875-3910页

作者： Ehteram, Mohammad Graf, Renata Ahmed, Ali Najah El-Shafie, Ahmed Semnan Univ Dept Water Engn Semnan Iran Adam Mickiewicz Univ Inst Phys Geog & Environm Planning Dept Hydrol & Water Management PL-61680 Poznan Poland Univ Tenaga Nasional UNITEN Dept Civil Engn Coll Engn Kajang 43000 Selangor Malaysia Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur 50603 Malaysia

Evaporation is one of the most important parameters of meteorological science. Therefore, predicting evaporation is necessary for both water resources and planning management. The present study uses Bayesian Model Averaging (BMA) based on developed and optimized Kernel Extreme Learning Machine (KELM) models for predicting daily evaporation in different provinces of Iran with different climates. The Water Strider Algorithm, Salp Swarm Algorithm, Shark Algorithm, and Particle Swarm optimization were combined with the KELM to predict daily evaporation in the Hormozgan, Mazandaran, Fars, Yazd, and Isfahan provinces. The models' inputs were average temperature, rainfall, number of sunny hours, wind speed, and relative humidity. The introducing a new hybrid gamma test for determining the adequate inputs, using hybrid and optimized KELM based on developing ELM for predicting evaporation, integrating individual models for predicting evaporation, and quantifying the uncertainty of outputs are the main innovations of the current study. Multiple error indices were used to evaluate the ability of models for predicting evaporation. The standalone and optimized KELM models were used to predict daily evaporation in the first level. In the next level, the BMA based on outputs of standalone and optimized KELM models predicted daily pan evaporation. The general results indicated that the BMA provided the best accuracy among other models in all stations. This study also introduced the new hybrid gamma test (GT-WSA) for choosing the best input combinations. The hybrid GT-WSA gave the best input combination without computing all input combinations (2(5) - 1). The uncertainty analysis of models also indicated that the uncertainty of BMA and optimized KELM models was lower than that of the KELM model.

关键词： Kernel Extreme Learning Machine model Evaporation optimization algorithms Bayesian Model Averaging

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Fast Desynchronization algorithms for Decentralized Medium Access Control Based on Iterative Linear Equation Solvers

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第11期67卷 6219-6226页

作者： Silvestre, Daniel Hespanha, Joao Silvestre, Carlos NOVA Univ Lisbon Sch Sci & Technol P-2829516 Caparica Portugal Lusdfona Univ Sch Sci & Technol COPELABS P-2829516 Caparica Portugal Univ Lisbon Inst Syst & Robot Inst Super Tecn P-2829516 Caparica Portugal Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Univ Macau Fac Sci & Technol Dept Elect & Comp Engn Macau 999078 Peoples R China

We tackle the problem of having multiple transmitters cooperating to be desynchronized using a distributed algorithm. Although this problem can also be found in surveillance, it has the most impact in achieving a fair access to a wireless shared communication medium at the medium access control layer in the context of wireless sensor networks. In this article, we first theoretically investigate the convergence rate of various optimization algorithms, giving closed-form expressions for the parameters achieving the best worst-case convergence rate. We then show that a recently proposed time-varying parameters Nesterov algorithm applied to this problem has worse performance assuming one can determine the number of sensors in the network. In order to remove such an assumption, the problem is seen as the solution of a linear equation corresponding to the first optimality condition. Both theoretically and in simulation, we show that using the Gauss-Seidel method improves the speed of convergence, although its performance degrades for large network sizes. In simulations, it is shown the behavior for various number of wireless devices, emphasizing how the algorithms actually perform in comparison with their worst-case theoretical rates for different network sizes.

关键词： Convergence optimization Wireless sensor networks Eigenvalues and eigenfunctions Mathematical models Transmitters Closed-form solutions Communication networks distributed control optimization algorithms

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Genetic Algorithm Based on Predation Search Strategy 2

Genetic Algorithm Based on Predation Search Strategy

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2nd International Conference on Mechanics, Electronics, Automation and Automatic Control, MEAAC 2024

作者： Ji, Wanfeng Xie, Jinpeng Naval Aviation University China

ISBN: (纸本)9781643685403

Genetic algorithm has been widely used in route planning problems due to its strong global search ability by simulating material changes in nature, as well as biological activities and evolution processes. Predation search strategy is a spatial search strategy used to solve combinatorial optimization problems. By simulating the predatory behavior of animal populations, it can effectively balance global exploration ability and local development ability. This article proposes a genetic algorithm based on predation strategy, which optimizes the search efficiency of the genetic algorithm by dynamically adjusting the crossover probability and mutation probability. Through example verification, this algorithm has better comprehensive search ability compared to basic genetic algorithms and adaptive genetic algorithms, and has been successfully applied in route planning. Â© 2024 The Authors.

关键词： optimization algorithms

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Novel Selection Indexes for Additional Selection of Non-dominated Sorting Genetic algorithms

Novel Selection Indexes for Additional Selection of Non-domi...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Hagihara, Leo Ohki, Makoto Faculty of Engineering Tottori University 101 4 Tottori680-0947 Japan

ISBN: (纸本)9798331533137

In this paper, we propose two new ranking indexes for solution set in many-objective optimization evolutionary algorithms. A non-dominated ranking allows selection pressure to perform effectively in Many-objective optimization Problems (MaOPs). NSGA is one of the algorithms that effectively incorporates non-dominated sorting for solving MaOPs. In MaOPs with a large number of objectives, most of the solutions in solution set of NSGA become non-dominated to one another. In such cases, NSGA no longer rank solutions with the non-dominated sorting, and is forced to optimize based only on additional selection indexes, for example, which is crowding distance in NSGA-II. It is well known that the additional selection with crowding distance does not effectively solve MaOP. NSGA-III allows niche selection by means of a large number of uniformly distributed reference points to realize the additional selection. While the niche selection yields high diversity in the solution set and improves the optimization, it requires huge computational cost for calculate the relation ship between the solutions and reference lines. To avoid these problems on conventional NSGAs, this paper proposes a new selection indexes to effectively solve MaOPs with the equivalent computational cost as NSGA-II, where the proposed indexes are based on the hyper volume difference and the hyper conical volume of each point in certain front set. These proposed indexes are implemented with significantly smaller computational costs than the niche selection of NSGA-III. NSGAs with the two proposed selection indexes are applied to several test MaOPs for comparing with the conventional NSGA-II and NSGA-III as numerical experiments. The experimental results showed that NSGA-III has been the most effective, but the proposed indexes demonstrated more effective optimization than NSGA-II. ©2024 IEEE.

关键词： optimization algorithms

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Accelerating Gradient Slope Computation with K1 Fluu: A Novel Technique for Neural Network Training 4

Accelerating Gradient Slope Computation with K1 Fluu: A Nove...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Chaudhary, Naina Gupta, Astha Singh, Gurinder Thakur, Ayush Patil, Pratap Paraji Sharma, Vinita Amity University Tashkent Uzbekistan Amity International Business School Amity University Uttar Pradesh Noida India Amity Institute of Information Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350387490

This report is all about the invention called the K1 Fluu, a new method in fast learning neural networks. K1 Fluu gets gradient slopes easier and yields hidden weights higher. It applies higher order derivatives, weight change, and a unique smoothing technique to improve the speed of the gradient slope. We demonstrate many difficult math operations that describe the essence of the functioning of K1 Fluu. I hope these equations make K1 Fluu understand how the partial derivatives to the parameters of the shapes of the loss function. We compared K1 Fluu on standard benchmarks and state-of-art neural networks. The testing confirms that K1 Fluu is more effective than other procedures. This makes the neural networks more precise, acquire knowledge within a faster time and also have a short training time. The performer K1 Fluu has the advantages in that it can change and smoothes things in order to make the learning process more stable and efficient. This report provides the field of neural network training with a powerful new tool. It enhances network performance and creates the need for other areas to be researched. © 2024 IEEE.

关键词： Dynamic Weighting Gradient Slope Acceleration Higher-Order Derivatives Neural Network Training optimization algorithms

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Sudoku Solver algorithms: A Comparative Analysis 1

Sudoku Solver Algorithms: A Comparative Analysis

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Manyam, Yashaswini Sundaram, Srinidhi Sahay, Apurvan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518943

The exploration of advanced algorithms for solving Sudoku puzzles reveals unique insights into their performance and effectiveness. The study analyzes and compares effectiveness in solving Sudoku puzzles with the classic backtracking method, an innovative approach based on ant colony optimization, and advanced constraint propagation algorithm. The project evaluates the performance of these algorithms about solution quality, computation time, and memory usage concerning the complexities of different puzzles: $9\times 9,\ 16\times 16$, and $25\times 25$ grid dimensions. Each individual of the different grid sizes tests time and space computations for each algorithm. These algorithms are putting up certain types of strengths and weaknesses in practical applications. The results, on average, produce that out of all, the ant colony optimization algorithm, which gives a good overall performance in terms of time and memory. © 2024 IEEE.

关键词： optimization algorithms

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Research on Index-based Octree Neighborhood Particle Search Algorithm 2

Research on Index-based Octree Neighborhood Particle Search ...

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Su, Mo Liu, Chang Jiang, Hong Shenyang Ligong University School of Information Science and Engineering Shenyang China The First Liaoning Provincial Hospital for Veterans and Disabled Soldiers Shenyang China

ISBN: (纸本)9798350368888

This paper proposes an index-based octree neighborhood particle search algorithm to optimize the efficiency of neighborhood particle search in Smoothed Particle Hydrodynamics (SPH) simulations. Based on the octree search algorithm, the optimization uses an index-based octree to reduce the depth of search recursion. In view of the variable particle number and variable particle support radius in the jet fire scene, the adaptive particle volume algorithm is combined. Experimental results show that the proposed algorithm performs better than the octree search algorithm when the number of particles is large. © 2024 IEEE.

关键词： optimization algorithms

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Constrained Multi-objective optimization Based on Dynamic Priority and Cooperative Offspring Generation

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IEEE Transactions on Evolutionary Computation 2025年

作者： He, Zhihui Wang, Feng Li, Bingdong Zhou, Aimin Wuhan University School of Computer Science Hubei 430072 China East China Normal University Shanghai Frontiers Science Center of Molecule Intelligent Syntheses Shanghai Institute of AI for Education School of Computer Science and Technology Shanghai200062 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science China

As the number and complexity of constraints in constrained multi-objective optimization problems (CMOPs) increase, the performance of existing constrained multi-objective evolutionary algorithms (CMOEAs) declines significantly. A novel idea is to sequentially address each constraint based on priority, effectively reducing the complexity of CMOPs. However, in these algorithms, the constraint-handling priority is determined statically in the initial stage. This may lead to inappropriate determination of constraint-handling priority since accurately estimating the constraint landscape in the initial stage is quite challenging. Moreover, these algorithms tackle constraints separately, neglecting the potential for inter-constraint cooperation and thus compromising their efficiency in constraint handling. Thus, we propose a constrained multi-objective evolutionary algorithm based on dynamic priority and cooperative offspring generation called DPCMOEA. Firstly, the constraint-handling priority is determined dynamically by the estimated inconsistency degree (EID) between the Pareto fronts of the candidate constraints and the current population. Secondly, computational resources are automatically allocated to each constraint according to EID based constraint relationship analysis. Finally, a new offspring generation strategy based on constraint cooperation is designed to enhance the quality of new solutions. Experimental results on six CMOP test suites demonstrate that DPCMOEA outperforms six state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： optimization algorithms

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