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International Conference on Science and Technology (TICST)

作者： Maji, K. B. Sree, U. Soumika Kar, R. Mandal, D. Ghoshal, S. P. NIT Dept Elect & Commun Engn Durgapur Durgapur India NIT Dept Elect Engn Durgapur Durgapur India

ISBN: (纸本)9781467376709

In this work, fourth order Butterworth analog active low pass filter is reported by employing seeker optimization algorithm (SOA). The passive elements i.e. capacitors and resistors values are most important for the design of analog active low pass filter. Evolutionary optimization technique is used to select the values of capacitors and resistors for the design of fourth order Butterworth active low pass filter. In this algorithm, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. The circuit component values of the filter are chosen in such a way so that they become compatible with the industrial series E12. The simulation results prove that SOA is superior for the minimization of error existing in the designed circuit with respect to the reported works.

关键词： Evolutionary optimization Technique seeker optimization algorithm Analog active filter Butterworth filter

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seeker optimization algorithm for Several Practical Applications

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2014年第2期7卷 353-359页

作者： Zhu, Yunfang Dai, Chaohua Chen, Weirong Southwest Jiaotong Univ Natl Engn & Technol Res Ctr Electrificat & Automa Sch Elect Engn Chengdu 610031 Peoples R China

optimization problems can often be simplified to the search for an optimal solution in the feasible search space. Based on the concept of simulating the act of human randomized search, a novel algorithm called seeker optimization algorithm (SOA) for real-parameter optimization is proposed in this paper. In the SOA, after given center point, search direction, search radius, and trust degree, every seeker moves to a new position (a candidate solution) from his current position based on his historical and social experiences. In this process, the update formula is like Y-conditional cloud generator. The algorithm's performance was studied using several typically complex functions. In all cases studied, SOA is superior to continuous genetic algorithm (CGA) greatly in terms of optimization quality, robustness and efficiency. At the same time, SOA greatly outperforms particle swarm optimization (PSO) in convergence speed. However, SOA needs more computation time. Simulations of designing both PID controller and IIR digital filter also show that SOA gets more satisfactory solutions with better evaluation values.

关键词： seeker optimization algorithm PID controller design IIR digital filter design

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A hybrid engineering algorithm of the seeker algorithm and particle swarm optimization

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MATERIALS TESTING 2022年第7期64卷 1051-1089页

作者： Liu, Haipeng Duan, Shaomi Luo, Huilong Kunming Univ Sci & Technol Kunming Yunnan Peoples R China

A newly hybrid algorithm is proposed based on the combination of seeker optimization algorithm and particle swarm optimization. The hybrid algorithm is based on a double population evolution strategy, and the populations of individuals are evolved from the seeker optimization algorithm and the particle swarm optimization separately. The populations of individuals employ an information sharing mechanism to implement coevolution. The hybrid algorithm enhances the individuals' diversity and averts fall into the local optimum. The hybrid algorithm is compared with particle swarm optimization, the simulated annealing and genetic algorithm, the dragonfly algorithm, the brain storming algorithm, the gravitational search algorithm, the sine cosine algorithm, the salp swarm algorithm, the multi-verse optimizer, and the seeker optimization algorithm, then 15 benchmark functions, five proportional integral differential control parameters models, and six constrained engineering optimization problems are selected for optimization experiment. According to the experimental results, the hybrid algorithm can be used in the benchmark functions, the proportional integral differential control parameters optimization, and in the optimization constrained engineering problems. The optimization ability and robustness of the hybrid algorithm are better.

关键词： engineering optimization problems function optimization hybrid algorithm particle swarm optimization seeker optimization algorithm

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Butterworth Filter Design using seeker optimization algorithm

Butterworth Filter Design using Seeker Optimization Algorith...

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International Conference on Science and Technology

作者： K. B. Maji U. Soumika Sree R. Kar D. Mandal S. P. Ghoshal Department of Electronics and Communication Engg. NIT Department of Electrical Engineering NIT

ISBN: (纸本)9781467376716

关键词： Evolutionary optimization Technique seeker optimization algorithm Analog active filter Butterworth filter Butterworth filters optimization algorithms changes position seekers Capacitors Resistor Device Component step size Service-oriented architecture Semiconductor Optical Amplifier Filters

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An improved decoding procedure and seeker optimization algorithm for reverse logistics network design problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第6期27卷 2703-2714页

作者： Subulan, Kemal Baykasoglu, Adil Saltabas, Alper Dokuz Eylul Univ Dept Ind Engn TR-35160 Izmir Turkey

Recently, Reverse Logistics (RL) and product recovery options such as recycling, remanufacturing and reusing have become important issues due to the environmental, economical issues and legal regulations. Due to this fact, companies should take into account the utilized recovery option while preparing their strategic planning activities (like network design) instead of using traditional production planning models. However, since RL network design problems are in the class of NP-hard, solving large scaled problems by exact algorithms is very difficult. Therefore, many meta-heuristics optimization algorithms have been proposed to provide near optimal solutions for supply chain, RL and closed-loop supply chain network design problems in the literature. In this paper, available decoding algorithms for solving generic RL design problems are revised so as to balance the problem without introducing any dummy node on the chromosome. Moreover, the proposed decoding procedure takes into account "equal transportation cost" situation. Then, a priority-based seeker optimization algorithm (SOA) which utilizes fuzzy reasoning procedure is developed for solution of the problem. In order to test performance of the algorithm, a numerical example is provided and obtained results are compared with particle swarm optimization (PSO) algorithm which is another swarm intelligence technique. Computational results show that SOA is superior to PSO in terms of both solution quality and computational time for the examined RL network design problem.

关键词： Reverse logistics network design seeker optimization algorithm particle swarm optimization fuzzy reasoning

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Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems

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NEUROCOMPUTING 2014年 143卷 197-207页

作者： Tuba, Milan Bacanin, Nebojsa Megatrend Univ Belgrade Fac Comp Sci Belgrade Serbia

seeker optimization algorithm is one of the recent swarm intelligence metaheuristics for hard optimization problems. It is based on the human group search behavior and it was successfully applied to various numerical optimization problems. While the seeker optimization algorithm was proven to be successful for different specific problems, it was not properly tested on a wide set of benchmark functions. Our testing on the standard well-known set of benchmark functions shows that the seeker optimization algorithm has serious problems with some types of functions. In this paper we introduced modifications to the seeker optimization algorithm to control exploitation/exploration balance and hybridized it with elements of the firefly algorithm that improved its exploitation capabilities. The firefly algorithm alone also exhibits deficiencies. Our proposed modified and hybridized seeker optimization algorithm not only overcame shortcomings of the original algorithms, but also outperformed other state-of-the-art swarm intelligence algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： seeker optimization algorithm Firefly algorithm Hybrid algorithms Constrained optimization Nature inspired algorithms Swarm intelligence

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Parameter estimation of chaotic systems by an oppositional seeker optimization algorithm

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NONLINEAR DYNAMICS 2014年第1期76卷 509-517页

作者： Lin, Jian Chen, Chang Zhejiang Univ Finance & Econ Sch Informat Hangzhou 310018 Peoples R China Zhejiang Univ Sch Elect Engn Hangzhou 310027 Peoples R China Hangzhou Dianzi Univ Sch Media & Design Hangzhou 310018 Peoples R China

The parameter estimation can be formulated as a multi-dimensional optimization problem. By combining the seeker optimization algorithm with the opposition-based learning method, an oppositional seeker optimization algorithm is proposed in this work, and is applied to the parameter estimation of chaotic systems. The seeker optimization algorithm provides a new alternative for population-based heuristic search. By considering an estimate and its opposite of current solutions at the same time, the opposition-based learning method is employed for population initialization and also for generation jumping in seeker optimization algorithm. Numerical simulations on two typical chaotic systems are conducted to show the effectiveness and robustness of the proposed scheme.

关键词： seeker optimization algorithm Parameter estimation Chaotic system Opposition-based learning

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The research of pattern synthesis of linear antenna array based on seeker optimization algorithm 6

The research of pattern synthesis of linear antenna array ba...

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6th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Wang, Bing Liu, Chao Wu, Huaning Naval Univ Engn Dept Commun Engn Wuhan Peoples R China

ISBN: (纸本)9781479962365

The seeker optimization algorithm (SOA) is adopted to optimize the pattern synthesis of linear antenna array and the conjugate gradient method is introduced to improve the local search ability of SOA algorithm. The side lobe level, the null depth and the pattern notches of the 20 element linear array pattern are optimized. The results are compared with the data obtained by Genetic algorithm (GA) and Particle Swarm optimization (PSO). Compared with the original SOA, GA and PSO, the proposed SOA has better performance.

关键词： seeker optimization algorithm array antennas pattern

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seeker optimization with Deep Learning Enabled Sentiment Analysis on Social Media

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Computers, Materials & Continua 2022年第12期73卷 5985-5999页

作者： Hanan M.Alghamdi Saadia H.A.Hamza Aisha M.Mashraqi Sayed Abdel-Khalek Department of Computer Science College of Computing Al-QunfidhahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science and HumanitiesSlayelPrince Sattam Bin AbdulAziz UniversitySaudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UnivesityNajran61441Saudi Arabia Department of Mathematics College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt

World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,*** Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and ***,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of ***,there is a need exists to develop a proper technique for both identification and classification of sentiments in social *** get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning *** current study introduces a seeker optimization algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social *** presented SOADL-SAC model involves the proper identification and classification of sentiments in social *** order to attain this,SOADL-SAC model carries out data preprocessing to clean the input *** addition,Glove technique is applied to generate the feature ***,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the ***,seeker optimization algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier *** order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark *** experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.

关键词： Sentiment analysis classification of sentiment social media seeker optimization algorithm glove embedding natural language processing

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Energy management and distribution of fuel cell hybrid power system based on efficient and stable movement of mobile robot

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 94卷 1064-1083页

作者： Lu, Xueqin Zhai, Xinrui Zhang, Yangyang Zhu, Chuanmin Qian, Shenchen Shanghai Univ Elect Power Sch Automat Engn Shanghai 200090 Peoples R China Jinan Polytech Coll Hebei 056046 Peoples R China Tongji Univ Sch Mech Shanghai 201804 Peoples R China

In response to the limitation of cable power supply on the movement range of mobile welding robots and the difficulty in achieving automatic welding of large equipment, a fuel cell hybrid power system is applied to the electric drive system of welding robots. State machines are used to determine the energy distribution state of hybrid power systems, while fuzzy algorithms are used to control the energy distribution of hybrid power in some states. In order to reduce the fluctuation of fuel cell power output and improve the economy of the energy supply system, a guidance head optimization algorithm is adopted to adjust the fuzzy membership function. Taking hydrogen consumption and fuel cell power output fluctuations as optimization objectives, and the SoC and fuzzy load deviation adjustment function of lithium-ion batteries as input variables, optimize the output power distribution of fuel cells and lithium-ion batteries. Compared with the optimization results of traditional guidance head optimization algorithms, particle swarm optimization algorithms, and fuzzy algorithms, the improved guidance head optimization algorithm can converge with fewer iterations while satisfying the dynamic response of robots, and can effectively improve the output power characteristics of fuel cells. Therefore, the control strategy studied in this article can effectively improve the output power characteristics of fuel cells, reduce output current ripple, and shorten response time. At the same time, it reduces the equivalent hydrogen consumption of the hybrid system and improves fuel economy.

关键词： PEMFC/Li-ion battery hybrid power system seeker optimization algorithm Equivalent hydrogen Energy management & optimization Mobile welding robot Seam tracking Seam tracking

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