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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Madhusoodanan, Devika Vismaya, R. Hridyalakshmi Sahay, Apurvanand Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

A comparative analysis of algorithms for the Traveling Salesman Problem (TSP) is proposed that evaluates four distinct approaches: Nearest neighbor (NN), 2-opt algorithm, Genetic Algorithm (GA), Simulated Annealing (SA) and a hybrid method of NN and 2-opt to determine its minimum distance achieved with minimum execution time. These algorithms are evaluated by their capability to find the shortest route and the total distance travelled in the given dataset. The evaluation process consists of execution time and route rendering. The results claim that the hybrid approach outsmarts all the other methods in terms of their execution time as it takes as low as 0.308 seconds as compared to other methods on the given dataset. This hybrid approach has the potential to improve the shortest distance in an iterative manner which balances the time versus distance objective criteria in a scalable manner. © 2024 IEEE.

关键词： 2-opt Compare Approaches Genetic Algorithm Hybrid Method Nearest neighbor optimization algorithms Simulated Cooling TSP

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Linear Convergence Rate Analysis of the (1+1)-ES on Locally Strongly Convex and Lipschitz Smooth Functions

Linear Convergence Rate Analysis of the (1+1)-ES on Locally ...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Morinaga, Daiki Fukuchi, Kazuto Sakuma, Jun Akimoto, Youhei University of Tsukuba RIKEN AIP Tsukuba Japan Tokyo Institute of Technology RIKEN AIP Tokyo Japan

ISBN: (纸本)9798400704956

Evolution strategy (ES) achieves widespread success in applications as a continuous black-box optimization algorithm. However, theoretical guarantee of its convergence rate has been done only inside convex quadratic functions and their monotonic transformations. In this study, we derive an upper bound and a lower bound of the rates of linear convergence of the (1+1)-ES on locally L-strongly convex and U-smooth functions, as exp [EQUATION] and [EQUATION], respectively. The order of the upper bound derived is competitive to that derived for a derivative-free optimization (DFO) algorithm in the previous study. Unlike DFO studies, any prior knowledge on the mathematical properties of the objective function such as Lipschitz constant is not given to the optimization algorithm. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Research on New Energy Grid Integration Planning and Economic Dispatch based on HS and PSO 24

Research on New Energy Grid Integration Planning and Economi...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Li, Yuanlin Fan, Li Power China Hubei Electric Engineering Co. Ltd China

ISBN: (纸本)9798400718137

With the rapid development of computer technology, various fields are using this technology to improve production efficiency, and the field of new energy grid integration planning and scheduling is also using computer technology. However, there are still problems of low utilization rate and poor economic benefits in the planning and economic dispatch models of new energy grid connection. In order to solve this problem, the particle swarm optimization algorithm is studied to optimize the acoustic search algorithm, and a new energy grid connection planning and economic dispatch model is established based on this optimization algorithm. Experiments were conducted on the improved algorithm, and the outcomes denoted that the accuracy of the improved algorithm was 0.98. Further experimental analysis was conducted on the new energy grid connection planning and economic dispatch model, and the outcomes denoted that the mean operating cost of the model was reduced by 33.33%, and the utilization rate of various new energy sources reached over 80%. The above results indicate that the optimization algorithm proposed in the study can not only improve the utilization rate of new energy, but also reduce the cost of the model. This study provides a scientific basis for promoting the use of new energy. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Assessing PV Integration with Evolutionary algorithms: Insights from the 2024 Competition on Evolutionary Computation in the Energy Domain

Assessing PV Integration with Evolutionary Algorithms: Insig...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Lezama, Fernando Almeida, José Soares, João Vale, Zita Bai, Wenlei Lee, Kwang Y. Zhang, Wen GECAD LASI Polytechnic of Porto Porto Portugal Oracle Energy and Water AustinTX United States Baylor University WacoTX United States Business School Baylor University WacoTX United States

ISBN: (纸本)9798400704956

In the field of energy systems, the "WCCI(CEC)/GECCO 2024 Competition Evolutionary Computation in the Energy Domain: Optimal PV System Allocation" serves as a platform for evaluating and comparing various metaheuristic algorithms tailored to address complex energy challenges. The current competition ranking metric, focused on mean fitness values, provides a basis for assessing algorithm performance. This work assesses several algorithms in a new testbed of the competition. The primary goal is to compare the performance of these algorithms based on their ability to address the challenges posed by the integration of PV systems in distribution networks. These results offer an early glimpse into how these metaheuristics perform, paving the way for further exploration of their capabilities. © 2024 is held by the owner/author(s).

关键词： optimization algorithms

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New Parallelized Greedy Soup Algorithm in the End-to-End Automatic Speech Recognition 7

New Parallelized Greedy Soup Algorithm in the End-to-End Aut...

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7th International Conference on algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Liu, Wei Chen, Chunyi Wei, Yuxin Shao, Zhuo School of Computer Science and Technology Changchun University of Science and Technology Changchun City China

ISBN: (纸本)9798331529314

This paper proposed a new algorithm in the end-to-end automatic speech recognition. For the end-to-end speech recognition model, we select greedy soup instead of the average model parameters in WeNet. We proposed a dynamic parallel greedy soup optimization algorithm to increase computational speed. The experiments show the importance of proposed method and optimization algorithm. The effectiveness is also proved on multiple corpora. © 2024 IEEE.

关键词： optimization algorithms

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A Centralized Strategy for Multi-Agent Exploration

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IEEE ACCESS 2022年 10卷 126871-126884页

作者： Gul, Faiza Mir, Adnan Mir, Imran Mir, Suleman Ul Islaam, Tauqeer Abualigah, Laith Forestiero, Agostino Air Univ Dept Elect Engn Aerosp & Aviat Campus KAMRA Attock 43600 Pakistan Univ Technol Sydney Fac Engn & IT Ultimo NSW 2007 Australia NUST Sch Av & Elect Engn Coll Aeronaut Engn Islamabad 24090 Pakistan Air Univ Dept Av Engn Aerosp & Aviat Campus KAMRA Attock 43600 Pakistan Fast Natl Univ Comp & Emerging Sci Elect Dept Peshawar 44000 Pakistan NUST Sch Aerosp & Mech Engn Coll Aeronaut Engn Islamabad 24090 Pakistan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 1183 Jordan Appl Sci Private Univ Fac Informat Technol Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Pulau Pinang Malaysia Natl Res Council Italy Inst High Performance Comp & Networking I-87036 Palermo Italy

This paper introduces recently developed Aquila optimization Algorithm specifically configured for Multi-Robot space exploration. The proposed hybrid framework "Coordinated Multi-Robot Exploration Aquila Optimizer " (CME-AO) is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based methodology, known as Aquila Optimizer (AO). A novel parallel communication protocol is also embedded 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. Numerous simulations under different environmental conditions were then performed to validate the effectiveness of the proposed algorithm. Algorithm consistency aspects in achieving the expected results (area explored rate and time) is demonstrated through statistical means. A perspective analysis is then performed by comparing the performance of the CME-AO algorithm with latest state of art contemporary algorithms namely conventional CME and CME-WO (CME Whale Optimizer). The comparison duly accommodates all pertinent aspects such as % area explored, number of failed runs, and time taken for map exploration for different environments. Results indicate that the proposed algorithm presents two distinct advantages over the other conventional state of the art CME based techniques a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time, with almost no fail runs. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other techniques either fails (stu

关键词： Multi-agent systems Robot kinematics Planning optimization Path planning Collision avoidance Robot sensing systems Artificial intelligence Autonomous systems Robotics multiple agent system space exploration artificial intelligence optimization algorithms autonomous systems

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Improved White Shark Algorithm Based on Chaotic Mapping and Quadratic Interpolation 5

Improved White Shark Algorithm Based on Chaotic Mapping and ...

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5th International Conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Pu, Die Zhang, Yaming Xie, Gaoyuan Pu, Dongqi Yunnan Normal University School of Information Yunnan Kunming China

ISBN: (纸本)9798331507992

To solve the problem of easily falling into local optima and low convergence accuracy of optimization results in white shark and optimization algorithms, an improved white shark optimization algorithm is proposed. In the population initialization phase, the sine chaotic mapping is used to initialize the population, enhancing the diversity of the population and improving the algorithm's search capability across the entire solution space;in the position update phase, a quadratic interpolation strategy is introduced to enhance the algorithm's local exploitation capability. Performance comparison experiments are conducted on 10 benchmark test functions, and the results show that the SWSO outperforms its comparative algorithms, with faster convergence speed and higher solution accuracy. © 2024 IEEE.

关键词： optimization algorithms

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acados-a modular open-source framework for fast embedded optimal control

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MATHEMATICAL PROGRAMMING COMPUTATION 2022年第1期14卷 147-183页

作者： Verschueren, Robin Frison, Gianluca Kouzoupis, Dimitris Frey, Jonathan van Duijkeren, Niels Zanelli, Andrea Novoselnik, Branimir Albin, Thivaharan Quirynen, Rien Diehl, Moritz ABB Corp Res Baden Switzerland Univ Freiburg IMTEK Freiburg Germany Katholieke Univ Leuven Dept Mech Engn Leuven Belgium Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Swiss Fed Inst Technol Inst Dynam Syst & Control Zurich Switzerland Mitsubishi Elect Res Labs Cambridge MA USA Univ Freiburg Fac Math Freiburg Germany

This paper presents the acados software package, a collection of solvers for fast embedded optimization intended for fast embedded applications. Its interfaces to higher-level languages make it useful for quickly designing an optimization-based control algorithm by putting together different algorithmic components that can be readily connected and interchanged. Since the core of acados is written on top of a high-performance linear algebra library, we do not sacrifice computational performance. Thus, we aim to provide both flexibility and performance through modularity, without the need to rely on automatic code generation, which facilitates maintainability and extensibility. The main features of acados are: efficient optimal control algorithms targeting embedded devices implemented in C, linear algebra based on the high-performance BLASFEO Frison (ACM Transactions on Mathematical Software (TOMS) 44: 1-30, 2018) library, user-friendly interfaces to Matlab and Python, and compatibility with the modeling language of CasADi Andersson (Mathematical Programming Computation 11: 136, 2019). acados is free and open-source software released under the permissive BSD 2-Clause license.

关键词： Direct optimal control optimization algorithms

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Optimal Wireless Meter Deployment Using Evolutionary algorithms 19

Optimal Wireless Meter Deployment Using Evolutionary Algorit...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Shakya, Siddhartha Poon, Kin Suliman, Ahmed Aljasmi, Alia Goian, Huda Barzaiq, Ahoud EBTIC Khalifa University of Science and Technology Abu Dhabi United Arab Emirates IoT & AI Operations Abu Dhabi United Arab Emirates

ISBN: (纸本)9789897587061

Utility companies use smart wireless meters to automate the collection of meter readings. This requires them to design and deploy a wireless meter network where each meter is connected to a central Data Concentrator Unit (DCU), which is then connected to the control centre of the company. In this paper we investigate the problem of wireless network meter deployment by means of evolutionary algorithms. We model the deployment problem as an evolutionary optimization problem, explore two different encoding schemes for the objective function, and test 4 different algorithms against 5 typical setups of the network in different areas. Our results show that Simulated Annealing (SA) is the best performing algorithm for the tested instances of the problem and has better reliability against the other compared algorithms The devised models and the algorithm have been built into a tool that is being used in a real-world scenario. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： optimization algorithms

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Research on microgrid scheduling based on Cauchy Variation improved Crow search algorithm 4

Research on microgrid scheduling based on Cauchy Variation i...

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

作者： Lee, Chun-Hsiang Yu, Ruiting Huang, Liang He, Zhengda Xie, Lieyong Wu, Chao Zhejiang Post and Telecom Engineering Construction Co Ltd Hangzhou China Zhejiang Communication Service Holding Group Co Ltd Hangzhou China Zhejiang University Hangzhou China

ISBN: (纸本)9798350368314

The Crow Search Algorithm (CSA) is known for its straightforward implementation and robust optimization capability, making it a popular choice for solving complex nonlinear problems. However, CSA has a notable limitation: its reliance on individual experience for searching candidate solutions, which becomes particularly pronounced when tackling high-dimensional functions. To address these shortcomings, this paper proposes an enhanced crow search algorithm (CSA-PSO) inspired by the principles of Particle Swarm optimization (PSO). The improved algorithm facilitates information sharing at both the individual and population levels, enabling crows to adopt a collaborative search strategy. Additionally, a Cauchy mutation mechanism is incorporated to prevent premature convergence. By applying Cauchy perturbation to the top 20% of individuals ranked by fitness value, the diversity of the population is maintained, ensuring a balanced approach between exploitation and exploration. To evaluate the effectiveness of CSA-PSO, the algorithm was tested on 17 benchmark functions and compared with other intelligent optimization techniques. Results demonstrate that CSA-PSO achieves superior performance in terms of search accuracy and convergence speed. Furthermore, the algorithm was successfully applied to energy scheduling in microgrids. By solving the objective function, the optimal power output scheme for distributed energy sources was determined, improving the economic efficiency of microgrid operations and validating the algorithm applicability. ©2024 IEEE.

关键词： optimization algorithms

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