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A novel whale optimisation algorithm with filtering disturbance and nonlinear step

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2022年第2期20卷 71-81页

作者： Luo, Jinkun He, Fazhi Li, Haoran Zeng, Xian-Tao Liang, Yaqian Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Wuhan Univ Ctr Evidence Based & Translat Med Zhongnan Hosp Wuhan 430071 Peoples R China

As a recent addition of population-based metaheuristic algorithms, whale optimisation algorithm (WOA) has attracted a lot of attention recently. However, WOA still has room for improvement in the accuracy and reliability of solution. In this study, a novel WOA with filtering disturbance and nonlinear step (FDNS-WOA) is proposed. Firstly, to enhance the population diversity and the global search ability, we design a weighted Cauchy mutation equation to perturb solution space. Secondly, the excessive disturbance may cause the imbalance between exploration and exploitation, and the quality of solutions will be decreased. Thus, before the disturbance, we propose a filtering mechanism to dynamically select different individuals for disturbance at different evolutionary stages. Thirdly, a weighted local walk mechanism is designed to improve the local exploitation capability. Different from original linear step with large fluctuation, we propose a nonlinear step to reduce blind spots in the local exploitation process. Finally, the proposed FDNS-WOA is tested on benchmark functions and applied in a real world problem. The experimental results show that the proposed FDNS-WOA not only outperforms other recent metaheuristic algorithms in term of accuracy and reliability for most benchmark functions, but also achieves satisfactory results in real world problem.

关键词： whale optimisation algorithm WOA Cauchy mutation economic load dispatch ELD metaheuristic algorithm population-based algorithm filtering disturbance nonlinear step

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Pelican Optimization algorithm: A Novel Nature-Inspired algorithm for Engineering Applications

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SENSORS 2022年第3期22卷 855-855页

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

Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization algorithm (POA) is introduced. The main idea in designing the proposed POA is simulation of the natural behavior of pelicans during hunting. In POA, search agents are pelicans that search for food sources. The mathematical model of the POA is presented for use in solving optimization issues. The performance of POA is evaluated on twenty-three objective functions of different unimodal and multimodal types. The optimization results of unimodal functions show the high exploitation ability of POA to approach the optimal solution while the optimization results of multimodal functions indicate the high ability of POA exploration to find the main optimal area of the search space. Moreover, four engineering design issues are employed for estimating the efficacy of the POA in optimizing real-world applications. The findings of POA are compared with eight well-known metaheuristic algorithms to assess its competence in optimization. The simulation results and their analysis show that POA has a better and more competitive performance via striking a proportional balance between exploration and exploitation compared to eight competitor algorithms in providing optimal solutions for optimization problems.

关键词： optimization nature inspired swarm intelligence optimization problem pelican population-based algorithm stochastic

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Thieves and Police, a New Optimization algorithm: Theory and Application in Probabilistic Power Flow

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IETE JOURNAL OF RESEARCH 2021年第6期67卷 951-968页

作者： Bagheri, Hajar Ara, Afshin Lashkar Hosseini, Rahil Islamic Azad Univ Dezful Branch Dept Elect Engn Power Engn Dezful Iran Islamic Azad Univ Dezful Branch Dept Elect Engn Dezful Iran Islamic Azad Univ Shahr E Qods Branch Dept Comp Engn Tehran Iran

This paper introduces a new meta-heuristic algorithm called Thieves and Police algorithm (TPA), for solving probabilistic optimization problems. The proposed algorithm is designed in five different phases each following a certain objective. In this algorithm, conventional matters between the thieves? team and the police are inspired as a social phenomenon. Motivation of thieves for stealing more properties is utilized in this algorithm to seek the optimum solution, and police track that result in fear and no risk taking of the thieves to enter areas under police surveillance is inspired to omit areas with weaker solutions. algorithm optimization procedure ends with apprehending the leader thief by police as the optimum solution. The performance of the TPA is evaluated in terms of local optima avoidance, exploration, exploitation, effectiveness, and convergence properties using a set of classical and modern test functions. Comparing the outcomes of other meta-heuristic optimization algorithms, the successful performance of the TPA in solving different optimization problems is confirmed. Moreover, the efficiency of the TPA in terms of solving problems with probabilistic nature is assessed by solving an optimization problem in power engineering area, known as Probabilistic Power Flow (PPF), in a Micro-Grid with renewable energy resources. based on the outcomes, the proficiency of the proposed algorithm is confirmed by solving the PPF problem and producing timely acceptable results. Hence, the TPA algorithm is suggested to solve complicated problems, with probabilistic nature, in the optimization field and other optimization problems.

关键词： Meta-heuristics Micro-grid Optimization population-based algorithm Thieves and police algorithm Probabilistic power flow

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Hybrid moth-flame optimisation algorithm with differential evolution for visual object tracking

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2022年第4期20卷 220-231页

作者： Reddy, K. Narsimha Bojja, Polaiah Koneru Lakshmaiah Educ Fdn Dept ECE Vaddeswaram 522502 Andhra Pradesh India

The improvement of the metaheuristic algorithms is one of the exciting topics to investigators in current years to resolve the optimisation and engineering problems. One of the population-based search methods, i.e., moth-flame optimisation algorithm (MFO), is eminent by easy execution, low limits, and high speed. On the other hand, the MFO process has shortcomings;for instance, the verdict of local minimum as a substitute for global minimum and weakness in global pursuit proficiency. In this paper, to resolve these shortages, the MFO algorithm is integrated with differential evolution (DE) and proposed a new hybrid method called MFO-DE. The exploration ability of the MFO algorithm is improved and existence trapped in the local minimum is prohibited by a mixture of the MFO and DE in the MFO-DE algorithm. The proposed algorithm was tested on the set of best-known unimodal and multimodal benchmark functions in various dimensions. Furthermore, MFO-DE is applied to visual object tracking as a real-life application.

关键词： population-based algorithm meta-heuristic differential evolution DE global optimisation visual object tracking

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Intelligent facial emotion recognition based on Hybrid whale optimization algorithm and sine cosine algorithm

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MICROPROCESSORS AND MICROSYSTEMS 2022年 95卷

作者： Lakshmi, A. Vijaya Mohanaiah, P. JNTUA Jawaharlal Nehru Technol Univ Anantapur Dept ECE Anantapur 515002 Andhra Pradesh India NBKR Inst Sci & Technol Dept ECE Vijayanagar Andhra Pradesh India

The Whale Optimization algorithm (WOA) is a new advanced algorithm that is based on the humpback whale chasing process. The main problem addressed by Whale Optimization algorithm, similar to other metaheuristic algorithms, is premature convergence. This paper introduces the Sine Cosine algorithm in the Whale Optimi-zation algorithm optimization process to enhance global convergence and to improve enactment. The proposed algorithm, called WOA-SCA, has local optima evasion of Whale Optimization algorithm and high capability to increase the exploration. The implementation of the proposed WOA-SCA algorithm is investigated through a series of tests on common benchmark test functions, with the results compared to those of six alternative al-gorithms. The results of the investigations show that the proposed WOA-SCA algorithm works better in concert with the outputs of the benchmark functions. Also, to validate the proficiencies of the WOA-SCA, it has been used to resolve the real-world problem of Facial Emotion Recognition (FER). The field of facial emotion recognition is a fascinating new area of research that gives us the ability to identify the human face's expression in natural settings. A large part of the standard methodologies certainly does not indicate the looks because the attitudes depend on the human face sections' behaviors. The paper proposes the efficient Facial Emotion Recognition method using MultiSVNNN based on WOA-SCA (the proposed WOA-SCA based Multi-Support Vector Neural Network). Using the ADFES dataset for the disgust expression, our results demonstrate that the proposed work yields average emotion recognition accuracy of 98% associated to 80% and 96% based on BBPSO and MFOPSO, respectively. Investigational consequences indicate the virtuous enactment of the proposed scheme resolutions regarding precision.

关键词： Meta-heuristic Optimization WOA-SCA WOA-SCA based MultiSVNN Facial emotion recognition population-based algorithm

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Collective Strategies With a Master-Slave Mechanism Dominate in Spatial-Iterated Prisoner's Dilemma

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2021年第4期12卷 45-56页

作者： Li, Jiawei Duncan, Robert Li, Jingpeng Bai, Ruibin Univ Nottingham Ningbo Peoples R China Univ Stirling Stirling Scotland

How cooperation emerges and persists in a population of selfish agents is a fundamental question in evolutionary game theory. The research shows that collective strategies with master-slave mechanism (CSMSM) defeat tit-for-tat and other well-known strategies in spatial iterated prisoner's dilemma. A CSMSM identifies kin members by means of a handshaking mechanism. If the opponent is identified as non-kin, a CSMSM will always defect. Once two CSMSMs meet, they play master and slave roles. A mater defects and a slave cooperates in order to maximize the master's payoff. CSMSM outperforms non-collective strategies in spatial IPD even if there is only a small cluster of CSMSMs in the population. The existence and performance of CSMSM in spatial iterated prisoner's dilemma suggests that cooperation first appears and persists in a group of collective agents.

关键词： Collective Behavior Cooperation Evolutionary Game Theory Handshaking Master Slave Mechanism population-based algorithm Prisoner's Dilemma Simulation Spatial Game Strategic Interaction

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Evaluation of inverse methods for estimation of mechanical parameters in solid tumors

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BIOMEDICAL PHYSICS & ENGINEERING EXPRESS 2020年第3期6卷 035027页

作者： Soltani, M. Jabarifar, Mohammad Kashkooli, Farshad Moradi Rahmim, Arman KN Toosi Univ Technol Dept Mech Engn Tehran Iran KN Toosi Univ Technol Computat Med Ctr Adv Bioengn Initiat Ctr Tehran Iran Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada Univ Waterloo Ctr Biotechnol & Bioengn CBB Waterloo ON Canada Univ Tehran Med Sci Canc Inst Iran Canc Biol Res Ctr Tehran Iran Univ Waterloo Dept Appl Math Waterloo ON Canada Univ British Columbia Dept Radiol & Phys Vancouver BC Canada BC Canc Res Ctr Dept Integrat Oncol Vancouver BC Canada

To treat cancer, knowledge of mechanical parameters can be essential. This study proposes a new approach for estimating hydraulic conductivity (k) and hydraulic conductivity ratio (alpha) of a living tissue, based on inverse methods, allowing tissue parameter estimation using only a limited set of measurements. First, two population-based algorithms (Levenberg-Marquardt (LM) method and conjugate gradient (CG) method) and two gradient-based algorithms (genetic algorithm (GA) and particle swarm optimization (PSO) algorithm) are considered, and a comparative study between these different inverse methods is performed to determine which methods have a good performance in terms of convergence rate and stability. CG method is shown to perform well in the case of noise-free input data;however, in the case of noisy input data, it fails to converge. The other three methods (LM, GA, and PSO) converge with estimation errors <10% in both noise-free and noisy input data, suggesting their utility to tackle this problem. In the second part, the effectiveness and good accuracy of these robust algorithms (LM, GA, and PSO) are validated with experimental results. The hydraulic conductivity and hydraulic conductivity ratio of a specific living tumor tissue are then estimated for mammary adenocarcinoma (R3230AC). Moreover, assuming measurement of only one-point interstitial pressure inside the tumor, the effect of the location of this one-point on estimation accuracy of hydraulic conductivity is investigated. We show that estimation errors for points measured near the surface and center of the tumor are greater than at other points.

关键词： solid tumor porous media inverse problem parameter estimation gradient-based algorithm population-based algorithm

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Projectiles Optimization: A Novel Metaheuristic algorithm for Global Optimization

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INTERNATIONAL JOURNAL OF ENGINEERING 2020年第10期33卷 1924-1938页

作者： Kahrizi, M. R. Kabudian, S. J. Razi Univ Dept Comp Engn & Informat Technol Kermanshah Iran

Metaheuristic optimization algorithms are a relatively new class of optimization algorithms that are widely used for difficult optimization problems in which classic methods cannot be applied and are considered as known and very broad methods for crucial optimization problems. In this study, a new metaheuristic optimization algorithm is presented, the main idea of which is inspired by models in kinematics. This algorithm obtains better results compared to other optimization algorithms in this field and is able to explore new paths in its search for desirable points. Hence, after introducing the projectiles optimization (PRO) algorithm, in the first experiment, it is evaluated by the determined test functions of the IEEE congress on evolutionary computation (CEC) and compared with the known and powerful algorithms of this field. In the second try out, the performance of the PRO algorithm is measured in two practical applications, one for the training of the multi-layer perceptron (MLP) neural networks and the other for pattern recognition by Gaussian mixture modeling (GMM). The results of these comparisons are presented in various tables and figures. based on the presented results, the accuracy and performance of the PRO algorithm are much higher than other existing methods.

关键词： Global Optimization Metaheuristic Optimization algorithm population-based algorithm Stochastic Optimization

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Impact of migration strategies and individual stabilization on multi-scale quantum harmonic oscillator algorithm for global numerical optimization problems

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APPLIED SOFT COMPUTING 2019年 85卷

作者： Ye, Xinggui Wang, Peng Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Sichuan Peoples R China Southwest Minzu Univ Sch Comp Sci & Technol Chengdu 610225 Sichuan Peoples R China China Unicom Fujian Branch Cloud Comp & Big Data Ctr Fuzhou 350007 Fujian Peoples R China

Multi-scale Quantum Harmonic Oscillator algorithm (MQHOA) is a population-based metaheuristic algorithm proposed recently. It has been proved effective and efficient to deal with unimodal and multimodal problems. Although the mechanism of replacing the worst particle with the fittest individual in MQHOA helps to fasten the iteration process, it can easily lead to premature convergence. Instead of direct replacement, several migration strategies are proposed to maintain the diversity of the population and help to obtain the global optima in difficult function evaluations. The impacts of the migration strategies and individual stabilization on the improvement of the algorithms in their effectiveness, reliability, accuracy and efficiency are well researched. A variety of multi-dimensional unimodal and multimodal benchmark functions are applied to illustrate the optimization performance of the proposed algorithms. Some of the best competitors in MQHOAs with migration strategies are selected to compare with several state-of-the-art stochastic algorithms. Experimental results presented suggest some conclusions: First, the individual stabilization mechanism does not significantly improve the performance of MQHOA. Second, random migration does not obviously help MQHOA perform much better. Third, migration strategies significantly affect the performance of MQHOA, and some of MQHOAs with migration strategies are very competitive to deal with numerical optimization problems. (C) 2019 Published by Elsevier B.V.

关键词： Multi-scale quantum harmonic oscillator algorithm population-based algorithm Stochastic algorithm Migration strategy Individual stabilization

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