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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Nim, Xintong Wei, Yiheng Zhou, Shuaiyu Tao, Meng College of Mathematics Southeast University Nanjing China

ISBN: (纸本)9798331517021

In many real-world applications, resource allocation in the presence of disturbances poses significant challenges due to the dynamic and uncertain nature of the environment. Traditional optimization algorithms often struggle to maintain robustness and efficiency under these conditions. This paper addresses the resource allocation problem with disturbances by proposing a novel distributed optimization algorithm. The proposed algorithm integrates the principles of proportional- integral-derivative (PID) control, discrete sliding mode control, and nabla fractional difference, resulting in enhanced robustness, flexibility, and efficiency. Through extensive numerical simu-lations, the correctness and effectiveness of the algorithm are thoroughly validated, demonstrating its potential to outperform existing approaches in disturbed environments. © 2024 IEEE.

关键词： optimization algorithms

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A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization

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ENERGY STRATEGY REVIEWS 2023年 49卷

作者： Shadmani, Alireza Nikoo, Mohammad Reza Gandomi, Amir H. Wang, Ruo-Qian Golparvar, Behzad Amirkabir Univ Technol Dept Maritime Engn Tehran Iran Univ Ghent Dept Electromech Syst & Met Engn Fac Engn & Architecture Technol Pk Zwijnaarde 46 Ghent Belgium Sultan Qaboos Univ Coll Engn Dept Civil & Architectural Engn Muscat Oman Univ Technol Sydney Fac Engn & Informat Technol Ultimo Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary Rutgers State Univ Dept Civil & Environm Engn New Brunswick NJ USA

Ocean energy technologies are in their developmental stages, like other renewable energy sources. To be useable in the energy market, most components of wave energy devices require further improvement. Additionally, wave resource characteristics must be evaluated and estimated correctly to assess the wave energy potential in various coastal areas. Multiple algorithms integrated with numerical models have recently been developed and utilized to estimate, predict, and forecast wave characteristics and wave energy resources. Each algorithm is vital in designing wave energy converters (WECs) to harvest more energy. Although several algorithms based on optimization approaches have been developed for efficiently designing WECs, they are unreliable and suffer from high computational costs. To this end, novel algorithms incorporating machine learning and deep learning have been presented to forecast wave energy resources and optimize WEC design. This review aims to classify and discuss the key characteristics of machine learning and deep learning algorithms that apply to wave energy forecast and optimal configuration of WECs. Consequently, in terms of convergence rate, combining optimization methods, machine learning, and deep learning algorithms can improve the WECs configuration and wave characteristic forecasting and optimization. In addition, the high capability of learning algorithms for forecasting wave resource and energy characteristics was emphasized. Moreover, a review of power take-off (PTO) co-efficients and the control of WECs demonstrated the indispensable ability of learning algorithms to optimize PTO parameters and the design of WECs.

关键词： Wave energy conversions Wave characteristics optimization algorithms Machine learning Deep learning

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Algorithm Performance Comparison for Integer-Valued OneMax

Algorithm Performance Comparison for Integer-Valued OneMax

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Li, Xiaoyue Kötzing, Timo University of Potsdam Hasso Plattner Institute Potsdam Germany

ISBN: (纸本)9798400704956

Recently, several continuous-domain optimizers have been employed to solve mixed-integer black box optimization (MI-BBO) problems by adjusting them to handle the discrete variables as well. In this work we want to compare how these adjusted algorithms perform on purely discrete variables when compared with algorithms designed to handle discrete *** use the algorithm RLSα,β from the literature, which was analyzed theoretically on a specific problem class. We experimentally optimize the parameters α and β for further ***, we make a comprehensive comparison of RLSα,β with algorithms from the continuous domain on a generalization of OneMax to ĝ.,Currency signn. We find that RLSα,β shows better performance on this discrete benchmark functions than the other ***, these results show that adaptations of continuous algorithms are not suited for MI-BBO problems, and that research combining the strengths of both approaches is more promising. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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A Novel Binary Emperor Penguin Optimizer for Feature Selection Tasks

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Computers, Materials & Continua 2022年第3期70卷 6239-6255页

作者： Minakshi Kalra Vijay Kumar Manjit Kaur Sahar Ahmed Idris Saban Oturk Hammam Alshazly Computer Science Department Government College BahadurgarhBahadurgarh124507India CSE Department National Institute of Technology Hamirpur177005India School of Engineering and Applied Sciences Bennett UniversityGreater Noida201310India College of Industrial Engineering King Khalid UniversityAbhaSaudi Arabia Department of Electrical and Electronics Engineering Amasya UniversityAmasyaTurkey Faculty of Computers and Information South Valley UniversityQena83523Egypt

Nowadays,due to the increase in information resources,the number of parameters and complexity of feature vectors *** offermore practical solutions instead of exact solutions for the solution of this *** Emperor PenguinOptimizer(EPO)is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor *** shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence *** traditional EPO overcomes the optimization problems in continuous search space,many problems today shift to the binary search ***,in this study,using the power of traditional EPO,binary EPO(BEPO)is presented for the effective solution of binary-nature *** algorithm uses binary search space instead of searching solutions like conventional EPO algorithm in continuous search *** this purpose,the sigmoidal functions are preferred in determining the emperor *** addition,the boundaries of the search space remain constant by choosing binary ***’s performance is evaluated over twenty-nine benchmarking *** evaluations are made to reveal the superiority of the BEPO *** addition,the performance of the BEPO algorithm was evaluated for the binary feature selection *** experimental results reveal that the BEPO algorithm outperforms the existing binary meta-heuristic algorithms in both tasks.

关键词： Metaheuristics optimization algorithms emperor penguin optimizer intensification diversification feature selection

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Energy Efficiency and Load optimization in Heterogeneous Networks through Dynamic Sleep Strategies: A Constraint-Based optimization Approach

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FUTURE INTERNET 2024年第8期16卷 262页

作者： Shabbir, Amna Shirazi, Muhammad Faizan Rizvi, Safdar Ahmad, Sadique Ateya, Abdelhamied A. NED Univ Engn & Technol Dept Elect Engn Karachi 75270 Pakistan Massachusetts Gen Hosp Wellman Ctr Photomed Boston MA 02114 USA Bahria Univ Dept Comp Sci Karachi Campus Karachi 75000 Pakistan Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci & Block Chain Lab Riyadh 11586 Saudi Arabia Zagazig Univ Dept Elect & Commun Engn Zagazig 44519 Egypt

This research endeavors to advance energy efficiency (EE) within heterogeneous networks (HetNets) through a comprehensive approach. Initially, we establish a foundational framework by implementing a two-tier network architecture based on Poisson process distribution from stochastic geometry. Through this deployment, we develop a tailored EE model, meticulously analyzing the implications of random base station and user distributions on energy efficiency. We formulate joint base station and user densities that are optimized for EE while adhering to stringent quality-of-service (QoS) requirements. Subsequently, we introduce a novel dynamically distributed opportunistic sleep strategy (D-DOSS) to optimize EE. This strategy strategically clusters base stations throughout the network and dynamically adjusts their sleep patterns based on real-time traffic load thresholds. Employing Monte Carlo simulations with MATLAB, we rigorously evaluate the efficacy of the D-DOSS approach, quantifying improvements in critical QoS parameters, such as coverage probability, energy utilization efficiency (EUE), success probability, and data throughput. In conclusion, our research represents a significant step toward optimizing EE in HetNets, simultaneously addressing network architecture optimization and proposing an innovative sleep management strategy, offering practical solutions to maximize energy efficiency in future wireless networks.

关键词： energy efficiency HetNets energy utilization efficiency (EUE) network performance wireless communication optimization algorithms stochastic geometry

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Fast random opposition-based learning Aquila optimization algorithm

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HELIYON 2024年第4期10卷 e26187页

作者： Gopi, S. Mohapatra, Prabhujit Vellore Inst Technol Sch Adv Sci Dept Math Vellore 632 014 Tamil Nadu India

Meta-heuristic algorithms are usually employed to address a variety of challenging optimization problems. In recent years, there has been a continuous effort to develop new and efficient meta-heuristic algorithms. The Aquila optimization (AO) algorithm is a newly established swarmbased method that mimics the hunting strategy of Aquila birds in nature. However, in complex optimization problems, the AO has shown a sluggish convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, in this study, a new mechanism named Fast Random Opposition-Based Learning (FROBL) is combined with the AO algorithm to improve the optimization process. The proposed approach is called the FROBLAO algorithm. To validate the performance of the FROBLAO algorithm, the CEC 2005, CEC 2019, and CEC 2020 test functions, along with six real-life engineering optimization problems, are tested. Moreover, statistical analyses such as the Wilcoxon rank-sum test, the t -test, and the Friedman test are performed to analyze the significant difference between the proposed algorithm FROBLAO and other algorithms. The results demonstrate that FROBLAO achieved outstanding performance and effectiveness in solving an extensive variety of optimization problems.

关键词： Opposition-based learning optimization algorithms Meta-heuristic algorithm Fast random opposition-based learning OBL FROBL

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A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle 41

A Study of First-Order Methods with a Deterministic Relative...

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41st International Conference on Machine Learning, ICML 2024

作者： Hallak, Nadav Levy, Kfir Y. The Faculty of Data and Decision Sciences The Technion Haifa Israel Viterby Faculty of Electrical and Computer Engineering The Technion Haifa Israel

This paper studies the theoretical guarantees of the classical projected gradient and conditional gradient methods applied to constrained optimization problems with biased relative-error gradient oracles. These oracles are used in various settings, such as distributed optimization systems or derivative-free optimization, and are particularly common when gradients are compressed, quantized, or estimated via finite differences computations. Several settings are investigated: optimization over the box with a coordinate-wise erroneous gradient oracle, optimization over a general compact convex set, and three more specific scenarios. Convergence guarantees are established with respect to the relative-error magnitude, and in particular, we show that the conditional gradient is invariant to relative-error when applied over the box with a coordinate-wise erroneous gradient oracle, and the projected gradient maintains its convergence guarantees when optimizing a nonconvex objective function. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Multi-objective optimal scheduling for DC microgrid systems 4

Multi-objective optimal scheduling for DC microgrid systems

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4th International Conference on Smart Grid and Energy Internet, SGEI 2024

作者： Dang, Haoyan Wu, Zhenkui Inner Mongolia University of Science & Technology School of Automation and Electrical Engineering Inner Mongolia Baotou China

ISBN: (纸本)9798350368314

In order to improve the economy of DC microgrid operation and battery service life, a multi-objective joint optimization model for DC microgrid is established, which integrally considers factors such as power generation cost, power purchase cost, power sales revenue, power balance constraints, charging and discharging time constraints, and so on. In addition, the corresponding model solving algorithm based on particle swarm algorithm is given. The validity of the proposed model and algorithm is verified. Finally, the superiority of the particle swarm optimization algorithm is demonstrated through experiments, and its daily operating cost is 11% lower than that of the genetic algorithm. ©2024 IEEE.

关键词： optimization algorithms

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Aquila Optimizer with Parallel Computation Application for Efficient Environment Exploration

Aquila Optimizer with Parallel Computation Application for E...

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AIAA Science and Technology (SciTech) Forum

作者： Gul, Faiza Mir, Imran Mir, Suleman Air Univ Aerosp & Aviation Campus Elect Dept Kamra Pakistan Natl Univ Sci & Technol Risalpur Pakistan Fast Natl Univ Comp Emerging Sci Elect Dept Peshawar Pakistan

ISBN: (数字)9781624106996

ISBN: (纸本)9781624106996

In the multiple fields covered by Artificial Intelligence (AI), robotic path planning is undoubtedly one of the issues that cover a wide range of research lines. This paper introduces recently developed Aquila optimization algorithm specifically configured for Multi-Robot space exploration. The framework is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based Aquila Optimizer (AO), combinely known as Coordinated Multi-robot Exploration Aquilla Optimizer (CME-AO). The proposed hybrid strategy also incorporates a novel parallel communication protocol, to improve multi-robot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila optimization technique is then incorporated to increase the overall solution accuracy. Algorithm validity and effectiveness was then validated utilizing different condition environment whose relative complexity was varied by varying parameters such as exploration space dimension and obstacle size, number and relative orientation. A perspective analysis is then performed to compare the performance of the proposed CME-AO algorithm with latest contemporary algorithms such as conventional CME and CME-WO (CME augmented Whale Optimizer). Results indicate efficacy of the proposed algorithm as it presents two distinct advantages a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other contemporary CME based techniques either fails (stuck locally) or takes longer exploration time.

关键词： Robotics Multiple Agent System Space Exploration Artificial Intelligence optimization algorithms Autonomous Systems

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Efficient Batched Algorithm for Contextual Linear Bandits with Large Action Space via Soft Elimination 37

Efficient Batched Algorithm for Contextual Linear Bandits wi...

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37th Conference on Neural Information Processing Systems (NeurIPS)

作者： Hanna, Osama A. Yang, Lin F. Fragouli, Christina Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781713899921

In this paper, we provide the first efficient batched algorithm for contextual linear bandits with large action spaces. Unlike existing batched algorithms that rely on action elimination, which are not implementable for large action sets, our algorithm only uses a linear optimization oracle over the action set to design the policy. The proposed algorithm achieves a regret upper bound (O) over tilde(root T) with high probability, and uses O(log log T) batches, matching the lower bound on the number of batches [13]. When specialized to linear bandits, our algorithm can achieve a high probability gap-dependent regret bound of (O) over tilde (1/Delta(min)) with the optimal log T number of batches, where Delta(min) is the minimum reward gap between a suboptimal arm and the optimal. Our result is achieved via a novel soft elimination approach, that entails "shaping" the action sets at each batch so that we can efficiently identify (near) optimal actions.

关键词： optimization algorithms

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