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optimal foraging algorithm Based on Differential Evolution

IEEE ACCESS 2020年 8卷 19657-19678页

作者： Fu, Yanming Zhang, Weizhi Qu, Chiwen Huang, Baohua Guangxi Univ Comp & Elect Informat Coll Nanning 530004 Peoples R China Baise Univ Sch Informat Engn Baise 533000 Peoples R China

The optimal foraging algorithm (OFA) was proposed by summarizing the rules of the animal foraging behavior in a group. Therefore OFA also has the defects of the swarm intelligence algorithm, such as easy to trap into local optimum and low convergence accuracy. In order to overcome these defects, an optimal foraging algorithm based on differential evolution (DEOFA) is proposed. The differential evolution mechanism contains mutation and crossover operators. The mutation and crossover operators are used to accelerate the convergence speed and global search capability of the OFA. The mutation operator is adopted to perform mutation operations centered on the optimal individual of each iteration to raise the convergence accuracy of the OFA. The test results of 30 benchmark functions show that the performance of DEOFA is better than nine compared algorithms in search accuracy, convergence speed and robustness. In order to verify the effectiveness of the DEOFA in solving practical problems, DEOFA is applied to solve the 0-1 knapsack problem. The test results in the six examples of 0-1 knapsack problems indicate that the DEOFA achieves better performance in accuracy, stability and high dimension.

关键词： Swarm intelligence algorithm optimal foraging algorithm differential evolution mutation and crossover 0-1 knapsack problem

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A novel chaotic optimal foraging algorithm for unconstrained and constrained problems and its application in white blood cell segmentation

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NEURAL COMPUTING & APPLICATIONS 2019年第11期31卷 7633-7664页

作者： Sayed, Gehad Ismail Solyman, Mona Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Giza Egypt

Meta-heuristic is defined as an iterative generation process with some random characters, which aims to generate a sufficiently good solution(s) for the global optimization problems. The process of solving a global optimization problem with near-optimal solutions is based on combining in some intelligent way different methodologies for exploiting and exploring the search space and building a structure information through different learning strategies. optimal foraging algorithm (OFA) is a robust meta-heuristic algorithm inspired by following the animal foraging behavior. Recently, chaos and meta-heuristics have been combined in different algorithms with the aim of overcoming the limitations of meta-heuristics. In this paper, a novel algorithm for combining chaos with OFA is presented. The presented chaotic optimal foraging algorithm (COFA) is introduced with a set of unconstrained and constrained optimization problems using different chaotic maps. The results show that the proposed COFA outperforms the standard OFA for these benchmarks regarding exploitation, exploration, the trajectory of foraging individuals, search history, fitness improvement of the population and convergence rate. The performance of COFA has also compared with other most recent and popular meta-heuristic algorithms proofing its superior. An application of white blood cell segmentation in microscopic images has been chosen, and the proposed chaotic optimal foraging algorithm has been applied to see its ability and accuracy to identify and segment the white blood cell for further diagnosis. According to the statistical analysis of objective values, COFA algorithm is more accurate and robust than original OFA algorithm. COFA proves its ability to converge to the optimal multiple thresholds level-based segmentation more accurate than OFA. The experimental results also show that the proposed COFA proves its high degree of stability compared with original OFA in finding optimal multiple threshold val

关键词： optimal foraging algorithm Global optimization Multi-level thresholding Kapur's method Chaos theory

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Improved optimal foraging algorithm for global optimization

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COMPUTING 2024年第7期106卷 2293-2319页

作者： Ding, Chen Zhu, Guangyu Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China Fuzhou Univ Sch Mech Engn & Automat Fuzhou Peoples R China Fuzhou Univ Qi Shan Campus2 Xue Yuan Rd Fuzhou Fujian Peoples R China

The optimal foraging algorithm (OFA) is a swarm-based algorithm motivated by animal behavioral ecology theory. When solving complex optimization problems characterized by multiple peaks, OFA is easy to get trapped in local minima and encounters slow convergence. Therefore, this paper presents an improved optimal foraging algorithm with social behavior based on quasi-opposition (QOS-OFA) to address these problems. First, quasi-opposition-based learning (QOBL) is introduced to improve the overall quality of the population in the initialization phase. Second, an efficient cosine-based scale factor is designed to accelerate the exploration of the search space. Third, a new search strategy with social behavior is designed to enhance local exploitation. The cosine-based scale factor is used as a regulator to achieve a balance between global exploration and local exploitation. The proposed QOS-OFA is compared with seven meta-heuristic algorithms on a CEC benchmark test suite and three real-world optimization problems. The experimental results show that QOS-OFA is better than other competitors on most of the test problems.

关键词： optimal foraging algorithm Quasi-opposition-based learning Social behavior Global exploration Local exploitation

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optimal foraging algorithm with direction prediction and Gaussian oscillation for constrained optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期205卷

作者： Jian, Zhong Quan Zhu, Guang Yu Fuzhou Univ Sch Mech Engn & Automat Fuzhou Peoples R China Fuzhou Univ Qi Shan Campus2 Xue Yuan Rd Fuzhou City Fujian Province Peoples R China

optimal foraging algorithm (OFA) is a newly stochastic optimization technique and is famous for its computational accuracy. However, the high computational accuracy leads to slow convergence speed. Experimental results demonstrate that OFA is good at unimodal functions but poor at multimodal functions. To improve these drawbacks, in this paper a novel modified OFA with direction prediction and Gaussian oscillation, named OFA/ P&G is introduced. In OFA/P&G, a transition matrix is constructed when a new global optimum is found to generate the candidate individuals. If the current global optimum does not change, the Gaussian oscillation is employed in a low probability and OFA update method is used in a high probability to generate the candidate individuals. The superior performance of OFA/P&G is verified on the 12 CEC2017 benchmark functions, 13 constrained benchmark functions and 5 engineering problems. Experimental results demonstrate that OFA/P&G outperforms other comparative algorithms. Finally, a real-world problem, drilling path optimization, is solved by OFA/P&G.

关键词： optimal foraging algorithm Transition matrix Differential evolution Constrained problem CEC2017

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Modified optimal foraging algorithm for Parameters Optimization of Support Vector Machine 3rd

Modified Optimal Foraging Algorithm for Parameters Optimizat...

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3rd International Conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： Sayed, Gehad Ismail Soliman, Mona Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Giza Egypt Sci Res Grp Egypt SRGE Giza Egypt

ISBN: (纸本)9783319746906;9783319746890

Support Vector Machine (SVM) is one of the widely used algorithms for classification and regression problems. In SVM, penalty parameter C and kernel parameters can have a significant impact on the complexity and performance of SVM. In this paper, an optimal foraging algorithm (OFA) is proposed to optimize the main parameters of SVM and reduce the classification error. Six public benchmark datasets were employed for evaluating the proposed (OFA-SVM). Also, five well-known and recently optimization algorithms are used for evaluation. These algorithms are Artificial Bee Colony (ABC), Genetic algorithm (GA), Chicken Swarm Optimization (CSO), Particle Swarm Optimization (PSO) and Bat algorithm (BA). The experimental results show that the proposed OFA-SVM obtained superior results. Also, the results demonstrate the capability of the proposed OFA-SVM to find optimal values of SVM parameters.

关键词： optimal foraging algorithm Parameter optimization Classification Support vector machine

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Solving many-objective reentrant hybrid flowshop scheduling problem considering uncertainty factors in thin-film transistor liquid crystal display

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 204卷

作者： Wu, Yongwei Lin, Xiufang Zhu, Guangyu Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350116 Fujian Peoples R China Minjiang Univ Coll Phys & Elect Informat Engn Fuzhou 350108 Peoples R China

The thin-film transistor liquid crystal display (TFT-LCD) front-end array manufacturing process exhibits reentrant characteristics, with uncertainties in the transportation of glass substrates during shop scheduling, further impacting carbon emissions. This study develops a reentrant hybrid flow shop scheduling model considering carbon emissions under uncertain transportation time, where the uncertain transportation time is specifically defined by a triangular fuzzy number (TFN), and a crossing reentrant job handling mechanism is proposed. According to the characteristics of the problem, the shop scheduling process is optimized. In scheduling optimization, the Pythagorean fuzzy set (PFS) is used to solve the problem of uncertain transportation time, and the MYCIN uncertainty factor method, originating from the MYCIN expert system, is employed to evaluate the scheduling scheme and assist metaheuristic algorithm decision-making. A many-objective decision-making method based on PFS and MYCIN uncertainty factors theory is proposed. The golden section factor and the Levy flight are introduced into optimal foraging algorithm (OFA). An improved OFA based on the PFS and MYCIN uncertainty factors (PMYCIN-OFA) is then designed. Finally, three types of experiments are conducted: test cases testing, factory application case testing, and industrial software Flexsim simulations. The results demonstrate that the PMYCIN-OFA surpasses the performance of five classical multi-objective intelligent optimization algorithms and can provide practical solutions in the actual TFT-LCD front-end array manufacturing workshop.

关键词： Reentrant hybrid flowshop Pythagorean fuzzy set Uncertainty factors theory Uncertain transportation time optimal foraging algorithm Thin-film transistor liquid crystal display manufacturing

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Solving a many-objective PFSP with reinforcement cumulative prospect theory in low-volume PCB manufacturing

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NEURAL COMPUTING & APPLICATIONS 2023年第27期35卷 20403-20422页

作者： Ding, Chen Qiao, Fei Zhu, GuangYu Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350116 Fujian Peoples R China

Permutation flow-shop scheduling problems with many objectives have wide applications in the modern manufacturing domain such as the printed circuit board (PCB) industry. In this paper, an upgraded method called reinforcement cumulative prospect theory is proposed for solving many-objective permutation flow-shop scheduling problems. Reinforcement cumulative prospect theory is determined by two reference points, the improved prospect value function, and the entropy-based decision weight function. A novel many-objective optimization algorithm, namely, an optimal foraging algorithm based on the reinforcement cumulative prospect theory (OFA/RCPT), is presented. The comprehensive prospect value is used as the fitness strategy of the OFA/RCPT algorithm to guide the optimization process. The performance of the proposed algorithm is assessed by a comparison with nine state-of-the-art algorithms. Three classification experiments are carried out on six Walking-Fish-Group test cases, seven permutation flow-shop scheduling benchmark instances, and a practical permutation flow-shop scheduling problem in low-volume PCB manufacturing. For the experiment, four performance metrics are adopted, and the commercial software Quest is used to simulate a PCB production line. The simulation results show that the proposed algorithm has better performance than the other algorithms.

关键词： Permutation flow-shop scheduling problems Reinforcement cumulative prospect theory optimal foraging algorithm Many-objective optimization

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A novel multilevel thresholding algorithm based on quantum computing for abdominal CT liver images

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EVOLUTIONARY INTELLIGENCE 2023年第2期16卷 439-483页

作者： Sayed, Gehad Ismail Canadian Int Coll CIC Sch Comp Sci New Cairo Egypt Cairo Univ Fac Comp & Artificial Intelligence Giza Egypt

Image segmentation is considered one of the important tasks for extracting useful information from an image. Multilevel thresholding image segmentation is one of the effective techniques for image segmentation. The computation time grows exponentially of traditional multilevel thresholding methods such as Kapur with the number of thresholds. Meta-heuristic algorithms based on swarm intelligence have proved their efficiency in solving the multilevel thresholding optimization problem. In this paper, a new hybrid algorithm based on quantum computing (QC) and optimal foraging algorithm (OFA) for multilevel image segmentation is presented. The main characteristic of the proposed quantum optimal foraging algorithm (QOFA) is the integration of quantum operators such as interference with the optimization process of OFA to get a proper balance between exploration and exploitation phases. The feasibility of the proposed multilevel image segmentation algorithm has been evaluated and tested on real abdominal CT liver images. All the results are analyzed qualitatively and quantitatively to evaluate the effectiveness and the robustness of the proposed algorithm. The experimental results revealed that the proposed algorithm is a very promising algorithm and it can provide better segmentation results compared with the standard OFA.

关键词： optimal foraging algorithm Global optimization Multilevel thresholding CT images Quantum mechanics

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Affine invariance of meta-heuristic algorithms

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INFORMATION SCIENCES 2021年 576卷 37-53页

作者： Jian, ZhongQuan Zhu, GuangYu Fuzhou Univ Sch Mech Engn & Automat Fuzhou Peoples R China

An algorithm whose performance depends on the objective function being aligned with a privileged coordinate system is a poor choice in general because it is unlikely that the opti-mal orientation will be known in advance. In this paper, a property of meta-heuristic algo-rithms, named affine invariance, is introduced to verify whether the algorithm is depended on the privileged coordinate system or not. The concept of affine invariance is described in detail, and some classical algorithms, efficient in most test and actual problems, are proved to be affine invariant. While some recent algorithms in the literature are proved to be not affine invariant. As a conclusion, particle swarm optimization (PSO), differential evolution (DE) and optimal foraging algorithm (OFA) are affine invariant, while grey wolf optimizer (GWO), sine cosine algorithm (SCA) and butterfly optimization algorithm (BOA) are not affine invariant. Furthermore, comparison tests are designed to support the theoretical analysis results. In these tests, same random numbers and initial population are used to avoid the influence of randomness, thus, the conclusion is reliable. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Particle swarm optimization Differential evolution optimal foraging algorithm Coordinate system Affine invariance Meta-heuristic algorithms

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