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Heterogeneous pigeon-inspired optimization

Science China(Information Sciences) 2019年第7期62卷 64-72页

作者： Hao WANG Zhuxi ZHANG Zhen DAI Jun CHEN Xi ZHU Wenbo DU Xianbin CAO School of Electronic and Information Engineering Frontier Institute of Science and Technology InnovationBeihang University School of Engineering and Materials Science Queen Mary University of London

Pigeon-inspired optimization(PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator,and the landmark operator, respectively. In canonical PIO, these two operators treat every bird equally,which deviates from the fact that birds usually act heterogenous roles in nature. In this paper, we propose a new variant of PIO algorithm considering bird heterogeneity — HPIO. Both of the two operators are improved through dividing the birds into hub and non-hub roles. By dividing the birds into two groups, these two groups of birds are respectively assigned with different functions of "exploitation" and "exploration", so that they can closely interact with each other to locate the best promising solution. Extensive experimental studies illustrate that the bird heterogeneity produced by our algorithm can benefit the information exchange between birds so that the proposed PIO variant significantly outperforms the canonical PIO.

关键词： heuristic optimization pigeon-inspired optimization particle heterogeneity network-based topology scale-free network selective-informed learning

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Design and optimization of 3D Radial Slot Grain Configuration

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Chinese Journal of Aeronautics 2010年第4期23卷 409-414页

作者： Ali Kamran School of Astronautics Beijing University of Aeronautics and Astronautics

Upper stage solid rocket motors （SRMS） for launch vehicles require a highly efficient propulsion system. Grain design proves to be vital in terms of minimizing inert mass by adopting a high volumetric efficiency with minimum possible sliver. In this arti- cle, a methodology has been presented for designing three-dimensional （3D） grain configuration of radial slot for upper stage solid rocket motors. The design process involves parametric modeling of the geometry in computer aided design （CAD） software through dynamic variables that define the complex configuration. Grain bum back is achieved by making new surfaces at each web increment and calculating geometrical properties at each step. Geometrical calculations are based on volume and change-in-volume calculations. Equilibrium pressure method is used to calculate the internal ballistics. Genetic algorithm （GA） has been used as the optimizer because of its robustness and efficient capacity to explore the design space for global optimum solution and eliminate the requirement of an initial guess. Average thrust maximization under design constraints is the objective function.

关键词： solid rocket motors 3D grains radial slot configuration internal ballistics computer aided design heuristic optimization genetic algorithm

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A new optimization algorithm applied in electromagnetics——Maxwell's equations derived optimization (MEDO)

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Science China(Information Sciences) 2020年第10期63卷 19-41页

作者： Donglin SU Lilin LI Shunchuan YANG Bing LI Guangzhi CHEN Hui XU School of Electronics and Information Engineering Beihang University Research Institute for Frontier Science Beihang University MIIT Key Laboratory of E3 for Smart System and Equipment Beihang University EMC Technology Institute Beihang University Shenyuan Honors Colledge Beihang University

In this paper, a novel global optimization algorithm, named as Maxwell’s equations derived optimization(MEDO), is proposed. Using the Maxwell’s equations to analyse the behaviors of the timevarying current, the Ampere force is obtained from Fleming’s left hand rule. MEDO introduces an ‘Ampere force’ term, which is derived from Maxwell’s equations and is rigorous in physics, to drive the variables to the global optimal solution in the search space. In addition, introducing ‘gravity’ to MEDO can increase the stability of the optimizations. 11 classical benchmarks are tested, and results show that MEDO can always converge to numerical optimal solutions. To evaluate the proposed MEDO in solving the electromagnetic problems, four practical engineering applications are considered including the linear antenna array synthesis,frequency selected surface optimization, numerical dispersion reduction for finite-difference method, and parameters extraction of typical waveform. These examples are significant in electromagnetics, but tough to be solved because of their high dimensionality and strong nonlinearity. Numerical results show that MEDO can outperform several classic optimization methods, like wind driven optimization(WDO) and particle swarm optimization(PSO). Therefore, the electromagnetics-inspired MEDO is robust and of great potential in solving the electromagnetic optimization problems.

关键词： electromagnetic theory heuristic optimization high-dimensionality Maxwell’s equations multimodal

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Construction and optimization of TRNG Based Substitution Boxes for Block Encryption Algorithms

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Computers, Materials & Continua 2022年第11期73卷 2679-2696页

作者： Muhammad Fahad Khan Khalid Saleem Mohammed Alotaibi Mohammad Mazyad Hazzazi Eid Rehman Aaqif Afzaal Abbasi Muhammad Asif Gondal Quaid e Azam University Islamabad45320Pakistan Foundation University Islamabad Islamabad44000Pakistan College of Business Administration University of TabukTabuk71491Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityAbha61413Saudi Arabia Department of Mathematics and Sciences Dhofar UniversitySalalah211Oman

Internet of Things is an ecosystem of interconnected devices that are accessible through the *** recent research focuses on adding more smartness and intelligence to these edge *** makes them susceptible to various kinds of security *** edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the *** this regard,block cipher has been one of the most reliable options through which data security is *** strength of block encryption algorithms against different attacks is dependent on its nonlinear primitive which is called Substitution *** the design of S-boxes mainly algebraic and chaos-based techniques are used but researchers also found various weaknesses in these *** the other side,literature endorse the true random numbers for information security due to the reason that,true random numbers are purely *** this paper firstly a natural dynamical phenomenon is utilized for the generation of true random numbers based ***,a systematic literature review was conducted to know which metaheuristic optimization technique is highly adopted in the current decade for the optimization of *** on the outcome of Systematic Literature Review(SLR),genetic algorithm is chosen for the optimization of *** results of our method validate that the proposed dynamic S-boxes are effective for the block ***,our results showed that the proposed substitution boxes achieve better cryptographic strength as compared with state-of-the-art techniques.

关键词： IoT security sensors data encryption substitution box generation True Random Number Generators(TRNG) heuristic optimization genetic algorithm

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Single and multi-area multi-fuel economic dispatch using a fuzzified squirrel search algorithm

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Protection and Control of Modern Power Systems 2021年第1期6卷 147-159页

作者： V.Ponnuvel Sakthivel P.Duraisamy Sathya Department of Electrical and Electronics Engineering Government College of EngineeringDharmapuriTamilnadu636704India Department of Electronics and Communication Engineering FEATAnnamalai UniversityChidambaramTamilnadu608002India

Multi-Area Multi-Fuel Economic Dispatch (MAMFED) aims to allocate the best generation schedule in each area and to offer the best power transfers between different areas by minimizing the objective functions among the available fuel alternatives for each unit while satisfying various constraints in power systems. In this paper, a Fuzzified Squirrel Search Algorithm (FSSA) algorithm is proposed to solve the single-area multi-fuel economic dispatch (SAMFED) and MAMFED problems. Squirrel Search Algorithm (SSA) mimics the foraging behavior of squirrels based on the dynamic jumping and gliding strategies. In the SSA approach, predator presence behavior and a seasonal monitoring condition are employed to increase the search ability of the algorithm, and to balance the exploitation and exploration. The suggested approach considers the line losses, valve point loading impacts, multi-fuel alternatives, and tie-line limits of the power system. Because of the contradicting nature of fuel cost and pollutant emission objectives, weighted sum approach and price penalty factor are used to transfer the bi-objective function into a single objective function. Furthermore, a fuzzy decision strategy is introduced to find one of the Pareto optimal fronts as the best compromised solution. The feasibility of the FSSA is tested on a three-area test system for both the SAMFED and MAMFED problems. The results of FSSA approach are compared with other heuristic approaches in the literature. Multi-objective performance indicators such as generational distance, spacing metric and ratio of non-dominated individuals are evaluated to validate the effectiveness of FSSA. The results divulge that the FSSA is a promising approach to solve the SAMFED and MAMFED problems while providing a better compromise solution in comparison with other heuristic approaches.

关键词： Fuzzy set theory heuristic optimization Multi-area economic dispatch Pareto-optimal front Squirrel search algorithm Tie-line constraint

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A novel pure data-selection framework for day-ahead wind power forecasting

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Fundamental Research 2023年第3期3卷 392-402页

作者： Ying Chen Jingjing Zhao Jiancheng Qin Hua Li Zili Zhang School of Economics and Management Harbin Institute of TechnologyHarbin 150001China School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Mechanical and Industrial Engineering Texas A&M University-KingsvilleTexas 78363USA

Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power *** study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework(PDF)to choose useful data prior to modeling,thus improving the accuracy of day-ahead wind power ***,we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting *** a small subset can increase selection flexibility,it can also produce billions of subset combinations,resulting in computational *** address this problem,we incorporated metamodeling and optimization steps into *** then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm,*** results demonstrate that(1)it is necessary to select data before constructing a forecasting model;(2)using a smaller subset will likely increase selection flexibility,leading to a more accurate forecasting model;(3)PDF can generate a better training dataset than similarity-based data selection methods(e.g.,K-means and support vector classification);and(4)choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.

关键词： Day-ahead wind power forecasting Data selection Design and analysis of computer experiments heuristic optimization Numerical weather prediction data

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