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A New Bipolar Approach Based on the Rooster Algorithm Developed for Utilization in Optimization Problems

APPLIED SCIENCES-BASEL 2025年第9期15卷 4921-4921页

作者： Gencal, Mashar Cenk Osmaniye Korkut Ata Univ Dept Management Informat Syst TR-80000 Osmaniye Turkiye

Meta-heuristic algorithms are computational methods inspired by evolutionary processes, animal or plant behaviors, physical events, and other natural phenomena. Due to their success in solving optimization problems, meta-heuristic algorithms are widely used in the literature, leading to the development of novel variants. In this paper, new swarm-based meta-heuristic algorithms, called Improved Roosters Algorithm (IRA), Bipolar Roosters Algorithm (BRA), and Bipolar Improved Roosters Algorithm (BIRA), which are mainly based on Roosters Algorithm (RA), are presented. First, the new versions of RA (IRA, BRA, and BIRA) were compared in terms of performance, revealing that BIRA achieved significantly better results than the other variants. Then, the performance of the BIRA algorithm was compared with the performances of meta-heuristic algorithms widely used in the literature, Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Grey Wolf Optimizer (GWO), and thus, its success in the literature was tested. Moreover, RA was also included in this test to show that the new version, BIRA, is more successful than the previous one (RA). For all comparisons, 20 well-known benchmark optimization functions, 11 CEC2014 test functions, and 17 CEC2018 test functions, which are also in the CEC2020 test suite, were employed. To validate the significance of the results, Friedman and Wilcoxon Signed Rank statistical tests were conducted. In addition, three commonly used problems in the field of engineering were used to test the success of algorithms in real-life scenarios: pressure vessel, gear train, and tension/compression spring design. The results indicate that the proposed algorithm (BIRA) provides better performance compared to the other meta-heuristic algorithms.

关键词： engineering design problems evolutionary computation meta-heuristics optimization

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Using Computer Graphics to Make Science Visible in Engineering Education

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2023年第5期43卷 99-106页

作者： Xie, Charles Ding, Xiaotong Jiang, Rundong Inst Future Intelligence Natick MA 01760 USA

Science plays a crucial role in engineering. But science tends to be obscure to students, especially when they are overwhelmed by complex engineering design challenges that involve many variables. This article shows how computer graphics can be used to visualize science concepts and operationalize inquiry practices in engineering design to support integrated learning and teaching of science and engineering. Based on these graphical capabilities, generative design driven by evolutionary computation can also be visually illustrated to give students a glimpse into how artificial intelligence is transforming engineering design. The article provides real-world examples in the field of sustainable energy engineering based on Aladdin, an open-source design and analysis Web app. It also presents evidence of learning from pilot tests at culturally diverse high schools. Science educators interested in incorporating engineering design into their lesson plans may find this article helpful.

关键词： Industries Visualization Codes Affordances evolutionary computation Design tools Artificial intelligence

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Data-driven evolutionary multi-task optimization for problems with complex solution spaces

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INFORMATION SCIENCES 2023年第1期626卷 805-820页

作者： Lyu, Chao Shi, Yuhui Sun, Lijun Southern Univ Sci & Technol 1088 Xueyuan Ave Shenzhen 518055 Peoples R China Univ Technol Sydney 15 Broadway Ultimo NSW 2007 Australia

Smoothing rugged and rough fitness landscapes by machine learning (ML) models is a feasible approach to improve the global optimization performance of evolutionary algorithms (EA). However, systematic studies on the smoothing performance of ML models are rare. Moreover, existing model management methods have a relatively low efficiency and can cause the error propagation problem. In this paper, we empirically evaluate and compare the performance of common ML models in smoothing high-dimensional fitness landscapes. Based on the experi-mental results, we design a novel framework called data-driven multi-task optimization (DDMTO) to enhance EAs' search abilities in complex solution spaces with ML smoothing models. The proposed framework firstly smooths the fitness landscape by a ML model. Then, it models the original fitness landscape optimization and the smoothed fitness landscape optimization as a two -task optimization problem and solves them with an evolutionary multi-task optimizer (EMTO). Coordinated by EMTO, the easy task can help optimize the difficult task through the proposed knowledge transfer operator, and the error propagation problem can be solved by the proposed knowledge transfer control scheme. To prove the effectiveness of the proposed methodology, we design several example algorithms by embedding different EAs and ML models into the DDMTO framework and compare their optimization performance on high-dimensional continuous benchmark functions with rugged and rough fitness landscapes and a real-world optimization problem. Experimental results show that, by embedding an EA into the DDMTO framework with an appropriate smoothing model, its exploration ability and global optimization performance in complex solution spaces can be significantly enhanced without increasing the total computational cost.

关键词： evolutionary computation Multi-task optimization Machine learning Fitness landscape smoothing

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Interaction-Based Prediction for Dynamic Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2023年第6期27卷 1881-1895页

作者： Liu, Xiao-Fang Xu, Xin-Xin Zhan, Zhi-Hui Fang, Yongchun Zhang, Jun Nankai Univ Inst Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China Ocean Univ China Sch Comp Sci & Technol Qingdao 266100 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Zhejiang Normal Univ Jinhua 321004 Peoples R China Hanyang Univ Ansan 15588 South Korea

Dynamic multiobjective optimization poses great challenges to evolutionary algorithms due to the change of optimal solutions or Pareto front with time. Learning-based methods are popular to extract the changing pattern of optimal solutions for predicting new solutions. They tend to use all variables as features (i.e., inputs) to build prediction models. However, there are usually some irrelevant and redundant variables, which increase training difficulty and decrease prediction accuracy. This article proposes a new interaction-based prediction (IP) method, which captures the correlation of variables with prediction targets and selects the most relevant variables to build prediction models using neural networks. In particular, the interaction between variables is detected to remove redundant variables. In addition, a correction procedure is developed to further improve predicted solutions according to the prediction error in past environments. The predicted solutions are used to update the population according to a specifically designed update strategy. Integrating the IP method into the framework of multiobjective evolutionary algorithm based on decomposition (MOEA/D), a new algorithm named IP-DMOEA is put forward. Experimental results on a typical dynamic multiobjective test suite demonstrate the better performance of the proposed IP-DMOEA than state-of-the-art algorithms in terms of convergence speed and solution quality. The proposed IP-DMOEA is also successfully applied to the multirobot task scheduling problem.

关键词： Correlation dynamic multiobjective optimization evolutionary computation interaction neural network prediction

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Levy flight-particle swarm optimization-assisted BiLSTM plus dropout deep learning model for short-term load forecasting

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NEURAL COMPUTING & APPLICATIONS 2023年第3期35卷 2679-2700页

作者： Kiruthiga, D. Manikandan, V Coimbatore Inst Technol Teaching Learning Ctr Coimbatore Tamil Nadu India Coimbatore Inst Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India

This paper proposes a new optimized Deep Learning (DL) network design for time series load forecasting. At first, DL's hyper parameters are optimized using the Levy flight-particle swarm optimization (LF-PSO) technique;then, the optimized DL model is used for load prediction. Furthermore, the results are compared with the existing state-of-the-art techniques to show prediction accuracy. Experiment and measured values indicate that the proposed new DL model is highly efficient for load prediction.

关键词： Load forecasting evolutionary computation Deep learning PSO Levy Flight BiLSTM LF-PSO

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A practical tutorial on solving optimization problems via PlatEMO

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NEUROCOMPUTING 2023年 518卷 190-205页

作者： Tian, Ye Zhu, Weijian Zhang, Xingyi Jin, Yaochu Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Bielefeld Univ Fac Technol Bielefeld Germany

PlatEMO is an open-source platform for solving complex optimization problems, which provides a variety of metaheuristics including evolutionary algorithms, swarm intelligence algorithms, multi-objective optimization algorithms, surrogate-assisted optimization algorithms, and many others. Due to the problem-independent nature of most metaheuristics, they are versatile for solving problems with various difficulties such as multimodal landscapes, discrete search spaces, multiple objectives, strict constraints, and expensive evaluations, regardless of the fields the problems belong to. Since PlatEMO was published in 2017, it has been used by many researchers from both academia and industry in the computational intelligence community. However, the basic terms and concepts about optimization may confuse practitioners and junior researchers new to metaheuristics. Hence, this paper presents a practical introduction to the use of PlatEMO 4.0, focusing on the procedures of defining problems, selecting suitable metaheuristics, and collecting results. Note, however, that a description of the technical details of metaheuristics is beyond the scope of this paper and interested readers may refer to the cited references. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Optimization Metaheuristics evolutionary computation Swarm intelligence Problem definition PlatEMO

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Bi-Directional Feature Fixation-Based Particle Swarm Optimization for Large-Scale Feature Selection

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IEEE TRANSACTIONS ON BIG DATA 2023年第3期9卷 1004-1017页

作者： Yang, Jia-Quan Yang, Qi-Te Du, Ke-Jing Chen, Chun-Hua Wang, Hua Jeon, Sang-Woon Zhang, Jun Zhan, Zhi-Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Vic Australia South China Univ Technol Sch Software Engn Guangzhou Guangdong Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Vic Australia Hanyang Univ Dept Elect & Commun Engn Ansan South Korea Zhejiang Normal Univ Jinhua Zhejiang Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China

Feature selection, which aims to improve the classification accuracy and reduce the size of the selected feature subset, is an important but challenging optimization problem in data mining. Particle swarm optimization (PSO) has shown promising performance in tackling feature selection problems, but still faces challenges in dealing with large-scale feature selection in Big Data environment because of the large search space. Hence, this article proposes a bi-directional feature fixation (BDFF) framework for PSO and provides a novel idea to reduce the search space in large-scale feature selection. BDFF uses two opposite search directions to guide particles to adequately search for feature subsets with different sizes. Based on the two different search directions, BDFF can fix the selection states of some features and then focus on the others when updating particles, thus narrowing the large search space. Besides, a self-adaptive strategy is designed to help the swarm concentrate on a more promising direction for search in different stages of evolution and achieve a balance between exploration and exploitation. Experimental results on 12 widely-used public datasets show that BDFF can improve the performance of PSO on large-scale feature selection and obtain smaller feature subsets with higher classification accuracy.

关键词： Feature extraction Correlation Big Data Bidirectional control Particle swarm optimization Faces Search problems Bi-directional feature fixation (BDFF) evolutionary computation feature selection large-scale particle swarm optimization (PSO)

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Firefly Based Energy Efficient Routing of Charging Request in Wireless Rechargeable Sensor Network

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2023年第4期39卷 869-884页

作者： Susan, Siron Anita T. Nithya, B. Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli 620015 India

The on-time charging of Rechargeable Sensor Nodes (RSNs) prolongs the lifetime of the Wireless Rechargeable Sensor Network (WRSN). However, it necessitates the prompt and accurate transmission of Charging Requests (CRs) from RSNs to Mobile Charging Drones (MCD), and accordingly the charging trip is planned by MCD. Most of the existing algorithms attempted to forecast the shortest path, which may not be an optimal path for the current network traffic. This causes increased packet loss, retransmissions thus consuming more energy. To address this, Firefly-based Optimal Path Selection Algorithm (FOPSA) is proposed to find an optimum and stable path for the on-time delivery of CRs to MCD using firefly optimization. The proposed fitness function utilizes geometric mean of the residual energy, distance, and hop count to predict the routing path for the transmission of CRs. It ensures that the path has potential RSNs with adequate energy to avoid routing holes. The proposed algorithm is simulated and compared with the existing algorithms in terms of throughput, packet loss, residual energy, and delay.

关键词： wireless sensor network wireless rechargeable sensor network mobile charging vehicle charging requests evolutionary computation firefly algorithm optimal path

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An Improved Genetic Algorithm for Constrained Optimization Problems

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IEEE ACCESS 2023年 11卷 10032-10044页

作者： Wang, Fulin Xu, Gang Wang, Mo Northeast Agr Univ Coll Engn Harbin 150030 Peoples R China

The mathematical form of many optimization problems in engineering is constrained optimization problems. In this paper, an improved genetic algorithm based on two-direction crossover and grouped mutation is proposed to solve constrained optimization problems. In addition to making full use of the direction information of the parent individual, the two-direction crossover adds an additional search direction and finally searches in the better direction of the two directions, which improves the search efficiency. The grouped mutation divides the population into two groups and uses mutation operators with different properties for each group to give full play to the characteristics of these mutation operators and improve the search efficiency. In experiments on the IEEE CEC 2017 competition on constrained real-parameter optimization and ten real-world constrained optimization problems, the proposed algorithm outperforms other state-of-the-art algorithms. Finally, the proposed algorithm is used to optimize a single-stage cylindrical gear reducer.

关键词： Optimization Statistics Social factors Genetic algorithms Linear programming evolutionary computation Search problems Genetic algorithm constrained optimization problem two-direction crossover grouped mutation

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Instance Selection-Based Surrogate-Assisted Genetic Programming for Feature Learning in Image Classification

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 1118-1132页

作者： Bi, Ying Xue, Bing Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

Genetic programming (GP) has been applied to feature learning for image classification and achieved promising results. However, many GP-based feature learning algorithms are computationally expensive due to a large number of expensive fitness evaluations, especially when using a large number of training instances/images. Instance selection aims to select a small subset of training instances, which can reduce the computational cost. Surrogate-assisted evolutionary algorithms often replace expensive fitness evaluations by building surrogate models. This article proposes an instance selection-based surrogate-assisted GP for fast feature learning in image classification. The instance selection method selects multiple small subsets of images from the original training set to form surrogate training sets of different sizes. The proposed approach gradually uses these surrogate training sets to reduce the overall computational cost using a static or dynamic strategy. At each generation, the proposed approach evaluates the entire population on the small surrogate training sets and only evaluates ten current best individuals on the entire training set. The features learned by the proposed approach are fed into linear support vector machines for classification. Extensive experiments show that the proposed approach can not only significantly reduce the computational cost but also improve the generalisation performance over the baseline method, which uses the entire training set for fitness evaluations, on 11 different image datasets. The comparisons with other state-of-the-art GP and non-GP methods further demonstrate the effectiveness of the proposed approach. Further analysis shows that using multiple surrogate training sets in the proposed approach achieves better performance than using a single surrogate training set and using a random instance selection method.

关键词： Training Feature extraction computational efficiency evolutionary computation Task analysis Optimization computational modeling evolutionary computation (EC) feature learning genetic programming (GP) instance selection surrogate

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