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hybrid anti-prematuration optimization algorithm

Journal of Systems Engineering and Electronics 2010年第3期21卷 503-508页

作者： Qiaoling Wang Xiaozhi Gao Changhong Wang Furong Liu Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin 150001 P. R. China Department of Electrical Engineering Helsinki University of Technology Otakaari 5 A Espoo 02150 Finland

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization *** paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear *** proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,*** operator,cloning operator,suppression operator,and receptor editing *** swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune *** simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence *** is also employed to cope with a real-world optimization problem.

关键词： hybrid optimization algorithm artificial immune system（AIS） particle swarm optimization（PSO） clonal selection anti-prematuration.

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A hybrid snow ablation optimized multi-strategy particle swarm optimizer for parameter estimation of proton exchange membrane fuel cell

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IONICS 2025年第5期31卷 4535-4562页

作者： Aljaidi, Mohammad Agrawal, Sunilkumar P. Parmar, Anil Jangir, Pradeep Arpita Trivedi, Bhargavi Indrajit Gulothungan, G. Jangid, Reena Alkoradees, Ali Fayez Zarqa Univ Fac Informat Technol Dept Comp Sci Zarqa 13110 Jordan Govt Engn Coll Dept Elect Engn Gandhinagar 382028 Gujarat India Shri KJ Polytech Dept Elect Engn Bharuch 392001 India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Graph Era Hill Univ Dept CSE Dehra Dun 248002 Uttarakhand India Graphic Era Dept CSE Dehra Dun 248002 Uttarakhand India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Vishwakarma Govt Engn Coll Ahmadabad Gujarat India SRM Inst Sci & Technol Dept Elect & Commun Engn SRM Nagar Kattankulathur 603203 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 Tamil Nadu India Qassim Univ Appl Coll Unit Sci Res Buraydah Saudi Arabia Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan 320315 Taiwan

The research presents Snow Ablation Optimized Multi-strategy Particle Swarm optimization (SAO-MPSO) as an algorithm to perform accurate parameter estimation of proton exchange membrane fuel cells (PEMFCs). The four optimization methods PSO, PPSO, AGPSO, and VPPSO fail to achieve proper exploration-exploitation balance which results in poor parameter tuning outcomes. SAO-MPSO assumes a framework where snow ablation search elements combine with multi-strategy reproduction methods to accelerate both speed-to-convergence and analysis precision. SAO-MPSO demonstrates excellent accuracy and stability when tested on six commercial PEMFC models under different operating conditions. SAO-MPSO demonstrates superior performance by reaching the lowest error metrics alongside the fastest convergence speed thus becoming an optimal optimization tool for PEMFC modeling. The obtained results demonstrate the reliability of this method for fuel cell parameter optimization which can lead to its application in real-time energy systems. The upcoming research will concentrate on developing SAO-MPSO for extensive fuel cell implementations and additional energy technology domains.

关键词： PEMFC Parameter optimization hybrid optimization algorithm Snow Ablation Optimized (SAO) Particle Swarm optimization (PSO)

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Server deployment strategies considering robustness and resource constraints in edge computing

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2025年第1期14卷 1-24页

作者： Cao, Junjie Yu, Zhiyong Cao, Min Zhu, Baohong Yang, Jian Xian Res Inst High Technol Xian 710025 Peoples R China Natl Univ Def Technol Coll Informat & Commun Wuhan 430035 Peoples R China Tencent Cloud Comp Xian Co Ltd Xian 710075 Shaanxi Peoples R China

Edge computing, as an emerging computing paradigm, efficiently offloads computationally intensive tasks to edge servers, extending services from the cloud center to the edge and significantly improving the efficiency of network data processing. However, existing research mostly focuses on the ideal deployment and optimization of edge servers, such as minimizing latency and load balancing, with little attention given to strategies for handling abnormal situations like soft attacks or sudden failures. This study delves into how to formulate server deployment strategies aiming to maximize system robustness and minimize costs when edge servers face soft attacks or sudden failures. Drawing insights from graph theory and network connectivity, we propose a server deployment strategy that enhances system robustness under resource constraints. Specifically, we introduce the concept of edge-delay-tolerant networks, ensuring the rapid establishment of a complete "backup link" in case of service interruption by constructing backup relay nodes and routing information tables to safeguard system continuity and stability. Additionally, we innovatively design adaptive edge server deployment methods in "silent" and "active" modes to cater to varying demands in different fault scenarios. To validate the effectiveness of the proposed strategies, we construct a multi-objective optimization problem through mathematical modeling and devise an hybrid optimization algorithm for solving it. Experimental simulation results demonstrate that the algorithm can provide near-optimal performance, effectively enhancing system robustness under resource constraints. This research not only offers new insights and methods for fault response mechanisms in edge computing but also provides valuable references for practical server deployment strategies, thereby promoting further development of edge computing technology.

关键词： Edge computing Edge server deployment Robustness hybrid optimization algorithm

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A hybrid algorithm and collaborative optimization strategy based on novel coding method for SPRD

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OCEAN ENGINEERING 2024年第Part1期311卷

作者： Yan, Wei-qiang Yang, Zhi-yao Yu, Yan-yun Lin, Yan Dalian Univ Technol Sch Naval Architecture & Ocean Engn Dalian 116024 Peoples R China

In previous research on ship pipe routing algorithms, the pipe coding methods always express pipes as straight segments. However, in actual design, pipes often need to be bent for space conservation, safety, or other reasons. Representing pipes only as straight segments deviates significantly from reality. Thus, this paper proposes a novel pipe coding method based on grid-free space, which can accurately represent a complete pipe composed of straight segments and bends. Additionally, we introduced Square strategy (SQS) into Differential Evolution strategy (DE) resulting in a novel signal objective hybrid algorithm called SQDE with high computational efficiency and strong global optimization ability. Furthermore, traditional ship pipe optimization focuses solely on the pipe itself, but considering both the pipe and its connected equipment during the optimization process can further improve the design quality of the entire pipe system. Therefore, we propose a novel cooperation optimization strategy based on a three-dimensional environment to enhance the design quality of the entire pipe system. Finally, the feasibility and reliability of the proposed approaches have been demonstrated through three simulation experiments.

关键词： hybrid optimization algorithm Ship pipe route design Coding technology Collaborative optimization

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A meta-optimized hybrid global and local algorithm for well placement optimization

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COMPUTERS & CHEMICAL ENGINEERING 2018年 117卷 209-220页

作者： Chen, Hongwei Feng, Qihong Zhang, Xianmin Wang, Sen Ma, Zhiyu Zhou, Wensheng Liu, Chen China Univ Petr East China Sch Petr Engn Qingdao 266580 Shandong Peoples R China CNOOC Res Inst Beijing 100027 Peoples R China

Well placement optimization is a complex and time-consuming task. An efficient and robust algorithm can improve the optimization efficiency. In this work, we propose a meta-optimized hybrid cat swarm mesh adaptive direct search (O-CSMADS) algorithm for well placement optimization. By coupling Cat Swarm optimization (CSO) algorithm, Mesh Adaptive Direct Search (MADS) algorithm, and Particle Swarm optimization (PSO) meta-optimization approach, O-CSMADS has global search ability and local search ability. We perform detailed comparisons of optimization performances between O-CSMADS, hybrid cat swarm mesh adaptive direct search (CSMADS) algorithm, CSO, and MADS in three different examples. Results show that O-CSMADS algorithm outperforms stand-alone CSO, MADS, and CSMADS. Besides, optimal controlling parameters are not same for different problems, which indicates that the optimization of algorithmic parameters is necessary. The proposed method also shows great potential for other petroleum engineering optimization problems, such as well type optimization and joint optimization of well placement and control. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Well placement optimization efficiency hybrid optimization algorithm Meta-optimization approach Cat swarm optimization algorithm Mesh Adaptive Direct Search

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A new approach to solve Economic Dispatch problem using a hybrid ACO-ABC-HS optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年第Jun.期78卷 735-744页

作者： Sen, Tanuj Mathur, Hitesh Datt BITS Dept Elect & Elect Engn Pilani 333031 Rajasthan India

This paper presents a newly developed hybrid optimization algorithm for solving the problem of Economic Dispatch (ED) for a multi-generator system. The hybrid algorithm named ACO-ABC-HS combines the framework of Ant Colony optimization (ACO), Artificial Bee Colony (ABC) and Harmonic Search (HS) algorithms to find the optimized solution for the system. The ACO algorithm is used to find the initial solution set, the ABC algorithm is employed to test and improve each of the probable solutions provided by the ACO module, while the HS module is used to discard the inferior solutions from the solution set and replace them with better ones. The performance of this hybrid algorithm is compared with those of conventional ED solving techniques like Gradient Search as well as other evolutionary algorithms namely ABC, ACO, HS and Particle Swarm optimization (PSO). Valve point loading, environmental emissions, line losses and ramping rate constraints have been included in the ED analysis to provide more practical results. The algorithm's performance is also tested for Multi-Area Economic Dispatch (MAED) with tie-line constraints. The results obtained clearly point out the superiority of the hybrid algorithm in finding out the optimum results, while satisfying the constraints of minimizing the generation costs, reducing the emissions as well as tie-line costs and transmission losses. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Economic Dispatch hybrid optimization algorithm Multi-Area Economic Dispatch (MAED) Ant Colony optimization (ACO) Artificial Bee Colony (ABC) Harmonic Search (HS)

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Scheduling of combined heat and generation outputs in power systems using a new hybrid multi-objective optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第14期32卷 10741-10757页

作者： Dolatabadi, Soheil El-Sehiemy, Ragab A. GhassemZadeh, Saeid Univ Tabriz Elect & Comp Engn Dept Tabriz Iran Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafr El Shaikh Egypt

In this paper, a hybrid optimization algorithm, consisted of weighted vertices-based optimizer (WVO) and particle swarm optimization (PSO) algorithm, is proposed to solve three economic frameworks for scheduling of power sources in order to meet the required power demand in power systems. These frameworks are economic power dispatch, economic emission power dispatch and combined heat and economic power dispatch problems. The basic idea of weighted vertices optimizer (WVO) is given from the bisection root-finding method in mathematics. It uses swarm intelligence and evolutionary strategy to efficiently find the optimum solution. However, the original WVO algorithm has some flaws in complex problems with a high number of variables and constraints. Therefore, this paper presents hybrid WVO-PSO algorithm which solved the mentioned flaws and also improved its speed and accuracy. In this algorithm, varying speed is defined for each vertex by using PSO which helps better exploration through the search space. To evaluate the performance of WVO-PSO, it is applied to some of well-known and complex emission/economic dispatch (EED), combined heat and power economic dispatch (CHPED) and combined heat and power emission/economic dispatch (CHPEED) problems and then the driven results are compared with other recent methods which demonstrates better performance of the proposed method in solving non-convex and constrained EED, CHPED and CHPEED problem in terms of minimizing costs and emissions.

关键词： Emission and economic dispatch Combined heat and power unit Multi-objective optimization Weighted vertices optimizer hybrid optimization algorithm

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Optimal operation of single and multi-reservoir systems via hybrid shuffled grey wolf optimization algorithm (SGWO)

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WATER SUPPLY 2022年第2期22卷 1663-1675页

作者： Masoumi, Fariborz Masoumzadeh, Sina Zafari, Negin Emami-Skardi, Mohammad Javad Univ Mohaghegh Ardabili Dept Civil Engn Ardebil Iran Univ Tabriz Fac Civil Engn Tabriz Iran Univ Tehran Sch Civil Engn Coll Engn Tehran Iran

Reservoir operation is a key issue in the water resources system. In this paper, the Shuffled Grey Wolf Optimizer (SGWO), a hybrid optimization algorithm inspired by Shuffled Complex Evolution (SCE-UA) and Gray Wolf Optimizer (GWO) algorithms, is introduced. The main modification in the proposed algorithm is how it divides and shuffles the population to enhance the information exchange among the individuals. The performance of the SGWO algorithm is compared to famous evolutionary algorithms such as Particle Swarm optimization (PSO) and Genetic algorithm (GA) in solving mathematical benchmark functions and multiple types of reservoir operation optimization problems with different scales. Two hypothetical 4 and10-reservoir system, and the Dez dam in Iran as a single reservoir system were selected as the case study in this research. The capability of the algorithms was compared in terms of accuracy of derived optimum objective function values, convergence speed, and stability of answers in different implementations. The results showed that the SGWO can reach considerably better results (0.3% to 26% better than the closest rival algorithms) using significantly lower number of function evaluations. It also showed the lowest standard deviation among other algorithms for all problems, which indicated the high reliability of this algorithm.

关键词： Grey Wolf optimization (GWO) hybrid optimization algorithm meta-heuristic algorithms reservoir operation shuffled complex evolution (SCE)

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A hybrid modified grey wolf optimization-sine cosine algorithm-based power system stabilizer parameter tuning in a multimachine power system

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OPTIMAL CONTROL APPLICATIONS & METHODS 2020年第4期41卷 1143-1159页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Indian Inst Technol ISM Dept Elect Engn Dhanbad Jharkhand India

Damping of low-frequency oscillations due to the unpredictable perturbations of a power network has always been a challenging task. In an interconnected power network, power system stabilizers (PSSs) are in practice to damp out these low-frequency oscillations by providing a necessary control signal to the automatic voltage regulator unit based on the deviation in generator speed/power output. This article proposes a novel approach of hybrid modified grey wolf optimization-sine cosine algorithm for tuning the parameters of PSS of an interconnected multimachine power system. The optimal parameter tuning of PSS with the proposed algorithm is achieved by considering a multiobjective function comprises of improving the damping and eigenvalue characteristics of the consolidated multimachine system. A benchmark model of two area four machine system is adopted to investigate the performance achieved with the proposed algorithm in the simultaneous damping of the local and interarea mode of oscillations in a multimachine power system. The system study has been carried out under a self-clearing fault condition, and the detailed analysis is presented by analyzing the eigenvalues, and their corresponding natural frequencies, damping ratios. The damping nature achieved for the system states under system uncertainties with the proposed algorithm is also presented. The performance obtained from the proposed hybrid algorithm has been compared with the standalone and state-of-the-art optimization methods.

关键词： hybrid optimization algorithm interarea oscillations low-frequency oscillation damping modified grey wolf optimization power system stabilizer sine-cosine algorithm

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Numerical optimal control for nonlinear dynamical systems involving mixed-valued inputs and joint probability path-constraints

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NONLINEAR DYNAMICS 2025年第4期113卷 3453-3483页

作者： Wu, Xiang Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

This paper proposes a numerical method for optimal control of nonlinear dynamical systems (OCNDSs) involving mixed-valued inputs (MVIs) and joint probability path-constraints (PPCs). By utilizing a transformation technology and a relaxation method, the original problem is first written as relaxation problem with only continuous-value inputs (CVIs). It is theoretically proved that the relaxation problem converges to the original problem. Then, a double-level approximation is proposed for the joint PPCs to obtain an approximate constrained optimal control problem (COCP) of the resulting relaxation problem and the convergence result of this approximation is established. Further, the resulting approximate problem is converted to a finite-dimensional problem (FDP) with bound constraints by using a control parametrization method, smoothing approximation, and penalty function. Following that, an improved sample approximation is proposed for transforming FDP into a deterministic problem while reducing computational costs. Finally, an improved greedy search strategy-based hybrid optimization algorithm (IGSSBHOA) is designed for OCNDSs based on the conjugate gradient method (CGM) and an improved greedy search strategy (IGSS), and its global convergence result is established. Anti-cancer drug scheduling (ACDS) is further generalized to demonstrate the validity of the proposed approach by considering the state variable/control input joint probability constraints. Simulation results illustrate that comparison against other typical approaches, the proposed approach not only has a better performance, but also is stable and robust.

关键词： Optimal control Nonlinear dynamical systems Mixed-valued inputs Joint probability path-constraints hybrid optimization algorithm

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