Article
Computer Science, Artificial Intelligence
C. Haider, F. O. de Franca, B. Burlacu, G. Kronberger
Summary: We describe and analyze algorithms for shape-constrained symbolic regression, which incorporate prior knowledge about the shape of the regression function. These algorithms are tested on physics models and compared to previous results achieved with single objective algorithms. The results show that the multi-objective algorithms can find mostly valid models even when using a soft-penalty approach. NSGA-II achieves the best overall rankings on instances with high noise.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Kirti Sharma, Vishnu Pratap Singh, Ali Ebrahimnejad, Debjani Chakraborty
Summary: Various optimization approaches have been developed and used for generating optimal solutions for different industry related optimization problems. The semantic representation of imprecise coefficients and various types of uncertainties arising in real life optimization problems are still a challenging task and require attention of academicians as well as professionals.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Somayeh Khezri, Salman Khodayifar
Summary: This study focuses on the minimum cost multi-commodity network flow (MCNF) problem in network optimization, which is complicated by the presence of uncertain parameters. A multi-objective approach is proposed, using random variables and Archimedean copula to model the uncertain parameters. The problem is then converted into a certain multi-objective problem using fuzzy programming and solved using second-order cone programming. The results demonstrate the effectiveness of the proposed approaches in solving large-scale network problems efficiently.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Information Systems
Zan Yang, Haobo Qiu, Liang Gao, Liming Chen, Jiansheng Liu
Summary: This paper proposes an adaptive surrogate-assisted MOEA/D framework (ASA-MOEA/D) for efficiently solving expensive constrained multi-objective optimization problems. With three specific search strategies, ASA-MOEA/D achieved targeted searches for different subproblems based on their optimization states. The framework maintained feasibility, convergence, and diversity through the use of RBF surrogates and exploration of unexplored subregions. Empirical studies showed that ASA-MOEA/D with tchebycheff approach outperformed four state-of-the-art algorithms.
INFORMATION SCIENCES
(2023)
Article
Engineering, Environmental
Youzhi Wang, Ping Guo
Summary: A copula-measure based interval multi-objective multi-stage stochastic chance-constrained programming (CMIMOMSP) model is proposed for water consumption optimization. It introduces multi-objective programming to improve the traditional stochastic chance-constrained programming by considering relationships among various factors. The model is applied to a case study in the Heihe River Basin, showing different impacts of optimistic-pessimistic factors on water allocation for different sectors.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Qian Wang, Neal N. Xiong, Song Jiang, Lu Chen
Summary: A surrogate-assisted evolutionary algorithm is proposed in this paper for solving expensive constrained multi-objective discrete optimization problems. By embedding random forest models and an improved stochastic ranking strategy, the algorithm makes significant progress in optimization efficiency and candidate solution quality.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xue Feng, Zhengyun Ren, Anqi Pan, Juchen Hong, Yinghao Tong
Summary: This paper proposes a multi-preference-based constrained multi-objective optimization algorithm, which determines preferences by analyzing evolution states and population characteristics, and implements them through reasonable scheduling evolutionary models. Compared to other algorithms, it performs better in diversity difficulty and convergence difficulty multi-objective optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Multidisciplinary
Yu Chen, Yonggang Li, Bei Sun, Chunhua Yang, Hongqiu Zhu
Summary: In this study, a multi-objective chance-constrained programming approach is proposed for blending optimization considering the uncertainty of zinc concentrates and the shortage of high-quality ore inventory. The effectiveness and feasibility of the method are verified using production data, showing that it can meet production needs and provide more accurate results compared to traditional methods.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2022)
Article
Computer Science, Artificial Intelligence
Liang Zhang, Kefan Wang, Luyuan Xu, Wenjia Sheng, Qi Kang
Summary: This study proposes a new MGP-based algorithm specifically designed for addressing imbalanced classification problems. The algorithm optimizes false positive rate, false negative rate, and tree size through an efficient evolutionary strategy. It also considers the performance of each classifier in majority and minority classes to make a weighted ensemble decision. Experimental results demonstrate that the proposed method outperforms existing approaches in imbalanced classification metrics.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zhun Fan, Zhaojun Wang, Wenji Li, Xiaomin Zhu, Bingliang Hu, An-Min Zou, Weidong Bao, Minqiang Gu, Zhifeng Hao, Yaochu Jin
Summary: Swarm robotic systems (SRSs) are used in various fields, and designing local interaction rules for self-organization of robots is a challenging task. This study proposes a modular design automation framework for gene regulatory network (GRN) models that can generate entrapping patterns without the need for expertise. The framework utilizes basic network motifs and multi-objective genetic programming to optimize the structures and parameters of the GRN models. Simulation results show that the framework can generate novel GRN models with simpler structures and better performance in complex environments. Proof-of-concept experiments using e-puck robots validate the feasibility and effectiveness of the proposed GRN models.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Green & Sustainable Science & Technology
Xian Huang, Wentong Ji, Xiaorong Ye, Zhangjie Feng
Summary: This paper proposes a multi-objective planning model based on chance-constrained programming to optimize the configuration of self-consistent energy systems for expressway electricity demand in no-grid areas with 100% renewable energy supply. The uncertainties of electric load and renewable energy sources are modeled using Monte Carlo Simulation and the backward reduction method. The Pareto solutions are optimized using the non-dominated sorted genetic algorithm-II, and the best solution is determined through the CRITIC and TOPSIS approach. The results from case studies demonstrate the effectiveness of the proposed method in enhancing system robustness and meeting power demand under confidence scenarios.
Article
Computer Science, Artificial Intelligence
Lei Zhu, Jian Lin, Yang-Yuan Li, Zhou-Jing Wang
Summary: This paper proposes an efficient decomposition-based multi-objective genetic programming hyper-heuristic approach for solving the multi-skill resource constrained project scheduling problem. The effectiveness of the proposed method has been validated through experiments.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Edgar Galvan, Leonardo Trujillo, Fergal Stapleton
Summary: This study proposes a method to introduce semantic-based distance in Multi-objective Genetic Programming to promote semantic diversity. When using highly unbalanced binary classification problems, this method can generate more non-dominated solutions and improve diversity, showing more statistically significant results compared to the other four methods.
APPLIED SOFT COMPUTING
(2022)
Article
Mechanics
Ricardo Fitas, Goncalo das Neves Carneiro, Carlos Conceicao Antonio
Summary: The inclusion of uncertainty in structural design optimization has led to more complex formulations, where uncertainty quantification significantly impacts solution methods and computing times. Designs that maintain steady levels of performance under uncertainty are referred to as robust, and the combination of both robustness and performance optimality is known as Robust Design Optimization (RDO). This study proposes a new approach to RDO for angle-ply composite laminate structures, utilizing a hybridization of Particle Swarm Optimization and Genetic Algorithms.
COMPOSITE STRUCTURES
(2023)