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Tasmanian Devil optimization: A New Bio-Inspired optimization algorithm for Solving optimization algorithm

IEEE ACCESS 2022年 10卷 19599-19620页

作者： Dehghani, Mohammad Hubalovsky, Stepan Trojovsky, Pavel Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic Univ Hradec Kralove Fac Sci Dept Appl Cybernet Hradec Kralove 50003 Czech Republic

In this paper, a new bio-inspired metaheuristic algorithm called Tasmanian Devil optimization (TDO) is designed that mimics Tasmanian devil behavior in nature. The fundamental inspiration used in TDO is simulation of the feeding behavior of the Tasmanian devil, who has two strategies: attacking live prey or feeding on carrions of dead animals. The proposed TDO is described, then its mathematical modeling is presented. TDO performance in optimization is tested on a set of twenty-three standard objective functions. Unimodal benchmark functions have analyzed the TDO exploitation capability, while high-dimensional multimodal and fixed-exploitation multimodal benchmark functions have challenged the TDO exploration capability. The optimization results indicate the high ability of the proposed TDO in exploration and exploitation and create a proper balance between these two indicators to effectively solve optimization problems. Eight well-known metaheuristic algorithms are employed to analyze the quality of the obtained results from TDO. The simulation results show that the proposed TDO, with its strong performance, has a higher capability than the eight competitor algorithms and is much more competitive. For further analysis, TDO is tested in optimizing four engineering design problems. Implementation results show that TDO has an effective performance in solving real-world applications.

关键词： Metaheuristics Search problems Mathematical models Games Statistics Sociology Problem-solving Bio-inspired exploitation exploration feeding optimization optimization algorithm Tasmanian devil

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Parallel chaos optimization algorithm with migration and merging operation

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APPLIED SOFT COMPUTING 2015年 35卷 591-604页

作者： Yuan, Xiaofang Zhang, Ting Xiang, Yongzhong Dai, Xiangshan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Chaos optimization algorithm (COA) Parallel chaos optimization algorithm (PCOA) Parameter identification

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A feature fusion optimization algorithm for predicting the remaining useful life of lithium-ion batteries

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ENERGY REPORTS 2023年 9卷 142-153页

作者： Zhang, Xinghong Xu, Yi Gong, Zehao Chongqing Univ Technol Sch Liang Jiang Int Chongqing 401135 Peoples R China

As the percentage of lithium-ion batteries as in a system for storing energy gradually rises, accidents brought by the deterioration of battery performance continue to occur. The solution to guaranteeing the steady operation of this system is learning how to precisely forecast the lithium-ion batteries' remaining useful life (RUL). A prediction framework that combines elements of incremental capacity analysis (ICA) and electrochemical impedance spectroscopy (EIS) is proposed to address issues of RUL. The framework initially examines the charging and discharging features of the battery before establishing a mapping association between fusion features and RUL using convolutional neural network (CNN) and improved long short-term memory network (ILSTM). The parameters of the improved particle swarm optimization algorithm (IPSO) are optimized to build the IPSO-CNN-ILSTM model by modifying updating rules of the inertia weight and learning factor of the particle swarm optimization (PSO) algorithm to improve its optimization ability. Lastly, numerical outcomes of the NASA PCoE datasets confirm this method's applicability and efficacy.(c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Lithium -ion battery Remaining useful life Health feature optimization algorithm

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An Effective Method for Segmentation of MR Brain Images Using the Ant Colony optimization algorithm

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JOURNAL OF DIGITAL IMAGING 2013年第6期26卷 1116-1123页

作者： Taherdangkoo, Mohammad Bagheri, Mohammad Hadi Yazdi, Mehran Andriole, Katherine P. Taba Med Imaging Ctr Shiraz Iran Harvard Univ Sch Med Ctr Evidence Based Imaging Dept RadiolBrigham & Womens Hosp Brookline MA 02446 USA Shiraz Univ Fac Elect & Comp Engn Dept Commun & Elect Shiraz Iran Shiraz Univ Med Sci Med Imaging Res Ctr Shiraz Iran

Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations;however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area.

关键词： Image processing Segmentation optimization algorithm Ant colony optimization

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Clouded Leopard optimization: A New Nature-Inspired optimization algorithm

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IEEE ACCESS 2022年 10卷 102876-102906页

作者： Trojovska, Eva Dehghani, Mohammad Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic

This paper proposes a new nature-inspired metaheuristic algorithm called Clouded Leopard optimization (CLO), which mimics the natural behavior of clouded leopards in the wild. The fundamental inspiration of CLO is derived from two ways of natural behaviors of the clouded leopard, including hunting strategy and daily resting on trees. CLO is mathematically modeled in two phases of exploration and exploitation, based on the simulation of these two natural behaviors. CLO performance is evaluated in solving sixty-eight benchmark functions, including unimodal, multimodal, CEC 2015, and CEC 2017 types. The performance of CLO in solving optimization problems is compared with the performance of ten famous metaheuristic algorithms. The simulation results show that the proposed CLO approach with high ability in exploration, exploitation, and balancing between them has a high capability in optimization applications. Simulation results show that CLO performs better in most test functions than competitor algorithms. In addition, the implementation of CLO on four engineering design issues demonstrates the capability of the proposed approach in real-world applications.

关键词： Metaheuristics Behavioral sciences Search problems Mathematical models Biological system modeling Statistics Social factors Clouded leopard optimization nature-inspired optimization algorithm metaheuristic exploration exploitation

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A new quantization optimization algorithm for the MPEG advanced audio coder using a statistical subband model of the quantization noise

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2006年第4期14卷 1328-1339页

作者： Derrien, O Duhamel, P Charbit, M Richard, G ENST TSI Signal & Image Proc Dept F-75634 Paris 13 France Ecole Super Elect LSS CNRS Signals & Syst Lab F-91192 Gif Sur Yvette France

In this paper, an improvement of the quantization optimization algorithm for the MPEG-Advanced Audio Coder (AAC) is presented. This algorithm, given a bit-rate constraint, minimizes the perceived distortion generated by the signal compression. The distortion can be related to the quantization error level over frequency subbands through an auditory model. Thus, optimizing the quantization requires knowledge of the rate-distortion function for each subband. When this function can be modeled in a simple way, the algorithm can take a one-loop recursive structure. However, in the MPEG AAC, the rate-distortion function is hard to characterize, since AAC makes use of nonlinear quantizers and variable length entropy coders. As a result, the standard algorithm makes use of two nested loops with a local decoder, in order to measure the error level rather than predicting its value. We first describe a partial subband modeling of the rate-distortion function of interest in the MPEG AAC. Then, using a statistical approach, we find a relationship between the error level and the so-called quantization "scale-factor" and propose a new algorithm that is basically similar to a classical one loop "bit allocation" process. Finally, we describe the complete algorithm and show that it is more efficient than the standard one.

关键词： bit-rate constraint distortion constraint optimization algorithm perceptual audio coding scale-factor statistical model subband quantization

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Improving multidimensional normal cloud model to evaluate groundwater quality with grey wolf optimization algorithm and projection pursuit method

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 354卷 120279页

作者： Yan, Jiaheng Ren, Ke Wang, Tao Huaiyin Inst Technol Fac Architecture & Civil Engn Huaian 223003 Peoples R China Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Groundwater quality is related to several uncertain factors. Using multidimensional normal cloud model to reduce the randomness and ambiguity of the integrated groundwater quality evaluation is important in environmental research. Previous optimizations of multidimensional normal cloud models have focused on improving the affiliation criteria of the evaluation results, neglecting the weighting scheme of multiple indicators. In this study, a new multidimensional normal cloud model was constructed for the existing onedimensional normal cloud model (ONCM) by combining the projection-pursuit (PP) method and the Grey Wolf optimization (GWO) algorithm. The effectiveness and robustness of the model were analyzed. The results showed that compared with ONCM, the new multidimensional normal cloud model (GWOPPC model) integrated multiple evaluation parameters, simplified the modeling process, and reduced the number of calculations for the affiliation degree. Compared with other metaheuristic optimization algorithms, the GWO algorithms converged within 20 iterations during 20 simulations showing faster convergence speed, and the convergence results of all objective functions satisfy the iteration accuracy of 0.001, which indicates that the algorithm is more stable. Compared to the traditional entropy weights (0.27, 0.23, 0.47, 0.44, 0.29, 0.59, 0.12) or principal component weights (0.38, 0.33, 0.42, 0.34, 0.47, 0.29, 0.38), the weight allocation scheme provided by the GWOPP method (0.50, 0.48, 0.05, 0.38, 0.02. 0.51 and 0.32) considers the density of the distribution of all samples in the data set space. Among all 55 groundwater samples, the GWOPPC model has 21 samples with lower evaluation ratings than the fuzzy evaluation method, and 28 samples lower than the Random Forest method or the WQI method, indicating that the GWOPPC model is more conservative under the conditions of considering fuzziness and randomness. This method can be used to evaluate groundwater quality in other

关键词： Groundwater quality Cloud model optimization algorithm Projection pursuit method

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A New Energy-Aware Method for Balancing the Load on Wireless IoT Devices Using an optimization algorithm Based on Chaos Theory

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期130卷 1677-1697页

作者： Siyi, Liu Sichuan Modern Vocat Coll Sch Elect & Informat Chengdu Sichuan Peoples R China

The number of connected devices contributing to the Internet of Things (IoT) has grown exponentially due to recent developments in wireless technology. The advent of IoT adds an entirely new category of applications to current services. Since the services are regulated by contact among objects, advantages are now being enhanced by utilizing these services. Many sensors and things are installed to track one or more activities. Hence, the load balancing protocol is essential in wireless IoT device architecture. To meet the QoS needs of IoT applications, it is crucial to measure, balance, analyze, and optimize these devices. Moreover, the IoT's vast amount of data and its processing can result in network outages. Studies on load balancing have primarily been conducted on cloud-based systems, and this challenge is an NP-hard problem. Consequently, this paper suggests a new energy-aware method for balancing the load on wireless IoT devices using a biogeography-inspired algorithm named Biogeography-Based optimization (BBO) based on chaos theory. The BBO algorithm can become trapped in local optima. Chaos theory is one of the most effective techniques for improving the performance of evolutionary algorithms in terms of both the avoidance of local optimums and the rate of convergence. Therefore, the combination of these algorithms is suggested in this paper to improve the efficiency of the balancing method. The effectiveness of the method is simulated using MATLAB. Related current methods are compared to the proposed method, and the findings showed substantial improvements in delay time and load balancing using the proposed technique. The proposed method has decreased the delay time and energy consumption by 7.58% and 15% compared to other methods.

关键词： Cloud computing Wireless technology Internet of Things Chaos theory optimization algorithm Load balancing IoT

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Analysis of thermal distribution in two end pumping Nd:YAG laser rod using bacterial foraging optimization algorithm

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PRAMANA-JOURNAL OF PHYSICS 2013年第3期80卷 487-505页

作者： Minshed, Mohammed A. Univ Technol Baghdad Laser & Optoelect Engn Dept Baghdad Iraq

There is a strong need for the optimized management of the thermal problem in Nd:YAG laser rod and for a powerful, fast, and accurate modelling tool capable of treating the heat source distribution very close to what it actually is. In this paper, a new optimization algorithm called bacterial foraging optimization algorithm (BFOA) is proposed for simulation of the radial heat distribution. A BFOA discloses a simulation method which delivers the exact temperature distribution in a circularly cylindrical structure with a circularly symmetrical, longitudinally, and transversally non-uniform heat source distribution and circularly symmetrical cooling means. The output power is obtained and compared with previously published experimental measurements for different pump power and a good agreement has been found.

关键词： Solid-state lasers thermal distribution bacterial foraging optimization algorithm

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Modeling of an Air-Core Compulsator and the Pulse Shaping Using optimization algorithm

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IEEE TRANSACTIONS ON PLASMA SCIENCE 2019年第1期47卷 611-618页

作者： Tao, Xuefeng Liu, Kun Wang, Haoze Space Engn Univ Dept Grad Management Beijing 101400 Peoples R China Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen 518101 Peoples R China

Compensated pulsed alternator (compulsator) plays a significant role in the field of pulsed power supply. Different kinds of compulsators have been used to drive high-energy weapons. In this paper, the mathematical model of a two-phase four-pole air-core compulsator is established, taking the current coupling, the change of rotor speed and changing load characteristics into account. Both the self-excitation process and the discharge process are modeled. Simulation results indicate that our model has high accuracy in comparison with the results of the co-simulation method using finite-element method and circuit principle. Moreover, the usage of the mathematical model can improve the simulation efficiency and flexibility. The current pulse requirements of electromagnetic rail gun (EMRG), flash lamp, and electro-thermal-chemical gun (ETCG) are analyzed, respectively, and the pulse shape optimization problem is studied based on the intelligent optimization algorithm. For EMRG and flash lamp, the term "acceleration ratio" is introduced to identify whether a pulse is flat or spiked. For ETCG, the conception of "shape variance" is proposed to evaluate the fitness of its pulse shape. With the help of suitable objection function and intelligent optimization algorithm, the optimal discharge pulse for specific load can be obtained easily.

关键词： Air-core compensated pulsed alternator (compulsator or CPA) mathematical model optimization algorithm pulse shaping techniques

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