Swarm and Evolutionary Computation最新文献

Hiding region constraints for black-box optimization. Application to camera placement in a virtual industrial environment using Evolutionary Computation

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-26 DOI: 10.1016/j.swevo.2025.101946

N.C. Cruz , M. Rouret , E.M. Ortigosa , E. Ros , J.A. Garrido

{"title":"Hiding region constraints for black-box optimization. Application to camera placement in a virtual industrial environment using Evolutionary Computation","authors":"N.C. Cruz , M. Rouret , E.M. Ortigosa , E. Ros , J.A. Garrido","doi":"10.1016/j.swevo.2025.101946","DOIUrl":"10.1016/j.swevo.2025.101946","url":null,"abstract":"<div><div>Many real-world optimization problems related to physical environments have heavily constrained search spaces, which hinders the direct application of meta-heuristics and similar black-box methods. This work describes how to avoid region constraints and self-adapt search spaces without renouncing competitive solutions. The proposal relies on defining a gateway function that hides environment-specific placement constraints and is compatible with regular meta-heuristics and simulation-based optimization. The function can show a standard box-constrained domain encapsulating the real places involved. It has been successfully applied to automatic camera placement for task observation in a particle accelerator. The environment and the process of interest are simulated in the Unity game engine, which defines a cutting-edge trend in the design of such facilities. The primary optimization method tested is the genetic algorithm of MATLAB’s Global Optimization Toolbox, an industry standard that achieves remarkable results. The widespread Teaching–Learning-Based Optimizer (TLBO) and a random search have also been tried to complement the study. According to the results, the proposal does not prevent the advanced optimizers from finding camera arrangements that outperform (and are validated by) a human expert. It also allows the random search to find reasonable arrangements despite the underlying intricate set of constraints.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101946"},"PeriodicalIF":8.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized framework for strategic electric vehicle charging station placement and scheduling in distribution systems with renewable energy integration

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-26 DOI: 10.1016/j.swevo.2025.101943

Jajna Prasad Sahoo, S. Sivasubramani, Parimi Sai Syama Srikar

{"title":"Optimized framework for strategic electric vehicle charging station placement and scheduling in distribution systems with renewable energy integration","authors":"Jajna Prasad Sahoo, S. Sivasubramani, Parimi Sai Syama Srikar","doi":"10.1016/j.swevo.2025.101943","DOIUrl":"10.1016/j.swevo.2025.101943","url":null,"abstract":"<div><div>Increased demand for electric vehicles (EVs) is faced with challenges by existing electrical grids. To address these challenges, in this study, a comprehensive framework is developed for the strategic placement of electric vehicle charging stations (EVCSs) in a distribution system and efficient charging and discharging schedules of EVs in the EVCSs. Optimal locations for EVCSs in a distribution system are identified with system inefficiencies and the inclusion of renewable energy sources (RESs), such as solar PV. EV scheduling is performed considering the power exchange between EVCSs and the grid with the integration of RESs in the distribution system. The framework is presented as an optimization problem and is addressed through the particle swarm optimization (PSO) approach. For comparison, the proposed model is also solved using the genetic algorithm (GA) and sine cosine algorithm (SCA). The IEEE 33 bus system is used as a test system to implement the suggested approach. The simulation outcomes show the effectiveness of the proposed model. The proposed PSO-based approach demonstrates significant improvements, reducing power losses by 10.29% for optimal EVCS placement compared to random placement, while also achieving cost reductions of 25.42% and 32% compared to SCA and GA, respectively, through optimized EVCS placement and scheduling. Validation through real-time implementation is performed using the OPAL-RT platform. The experimental setup confirms the real-time feasibility of the suggested approach.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101943"},"PeriodicalIF":8.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A difference vector angle dominance relation for expensive multi-objective optimization

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-26 DOI: 10.1016/j.swevo.2025.101924

Cuicui Yang, Jing Chen, Junzhong Ji, Xiaoyu Zhang, Kangning Hao

{"title":"A difference vector angle dominance relation for expensive multi-objective optimization","authors":"Cuicui Yang, Jing Chen, Junzhong Ji, Xiaoyu Zhang, Kangning Hao","doi":"10.1016/j.swevo.2025.101924","DOIUrl":"10.1016/j.swevo.2025.101924","url":null,"abstract":"<div><div>For the latest two years, relation classification-based surrogate assisted evolutionary algorithms show good potential for solving expensive multi-objective optimization problems (EMOPs). However, the existing studies are still at the initial stage and lack specific research on the dominance relation. This paper proposes a difference vector angle dominance relation for EMOPs, which uses an angle threshold <span><math><mi>φ</mi></math></span> to control the selection pressure and is called DVAD-<span><math><mi>φ</mi></math></span>. The proposed DVAD-<span><math><mi>φ</mi></math></span> has adaptive selection pressure and considers the convergence and diversity of solutions when picking out superior solutions, which makes it beneficial to pick out promising solutions for expensive real FEs and reduce expensive real FEs. To be specific, we firstly give the definition of DVAD-<span><math><mi>φ</mi></math></span> that measures the superiority from one solution to another solution according to the angle threshold <span><math><mi>φ</mi></math></span>. Then, we deduce that there is monotonicity between the angle threshold <span><math><mi>φ</mi></math></span> and the number of non-dominated solutions in the sense of DVAD-<span><math><mi>φ</mi></math></span>. At last, we propose an adaptive determination strategy of angle threshold based on bisection to set proper pressure for picking out promising solutions for expensive real FEs. Experiments have been conducted on 23 test functions from two benchmark sets and one real-world problem. The experimental results have verified the effectiveness of DVAD-<span><math><mi>φ</mi></math></span>.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101924"},"PeriodicalIF":8.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A subspace strategy based coevolutionary framework for constrained multimodal multiobjective optimization problems 基于子空间策略的受约束多模态多目标优化问题协同进化框架

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-24 DOI: 10.1016/j.swevo.2025.101941

Li Yan , Shunge Guo , Jing Liang , Boyang Qu , Chao Li , Kunjie Yu

引用次数: 0

A comparison-relationship-surrogate evolutionary algorithm for multi-objective optimization

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-24 DOI: 10.1016/j.swevo.2025.101947

Christopher M. Pierce , Young-Kee Kim , Ivan Bazarov

{"title":"A comparison-relationship-surrogate evolutionary algorithm for multi-objective optimization","authors":"Christopher M. Pierce , Young-Kee Kim , Ivan Bazarov","doi":"10.1016/j.swevo.2025.101947","DOIUrl":"10.1016/j.swevo.2025.101947","url":null,"abstract":"<div><div>Evolutionary algorithms often struggle to find well converged (e.g small inverted generational distance on test problems) solutions to multi-objective optimization problems on a limited budget of function evaluations (here, a few hundred). The family of surrogate-assisted evolutionary algorithms (SAEAs) offers a potential solution to this shortcoming through the use of data driven models which augment evaluations of the objective functions. A surrogate model which has shown promise in single-objective optimization is to predict the “comparison relationship” between pairs of solutions (i.e. who’s objective function is smaller). In this paper, we investigate the performance of this model on multi-objective optimization problems. First, we propose a new algorithm “CRSEA” which uses the comparison-relationship model. Numerical experiments are then performed with the DTLZ and WFG test suites plus a real-world problem from the field of accelerator physics. We find that CRSEA finds better converged solutions than the tested SAEAs on many of the <em>medium-scale, biobjective</em> problems chosen from the WFG suite suggesting the “comparison-relationship surrogate” as a promising tool for improving the efficiency of multi-objective optimization algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101947"},"PeriodicalIF":8.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analysis of SAT constrained MNK-landscapes as benchmark problems for multi-objective evolutionary algorithms

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-22 DOI: 10.1016/j.swevo.2025.101933

Felipe Honjo Ide , Hernan Aguirre , Minami Miyakawa , Darrel Whitley

引用次数: 0

An adaptive large neighborhood search with multi-deletion operators for multi-depot green vehicle routing problem with time windows

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-20 DOI: 10.1016/j.swevo.2025.101942

Yukang Su , Shuo Zhang , Yang Wang , Xing Cui , Yuyang Wu

{"title":"An adaptive large neighborhood search with multi-deletion operators for multi-depot green vehicle routing problem with time windows","authors":"Yukang Su , Shuo Zhang , Yang Wang , Xing Cui , Yuyang Wu","doi":"10.1016/j.swevo.2025.101942","DOIUrl":"10.1016/j.swevo.2025.101942","url":null,"abstract":"<div><div>In this paper, we proposed an adaptive large neighborhood search algorithm with multiple deletion operators (ALNS-MDO) to solve the multi-depot green vehicle routing problem with time windows, considering multi-depot sharing and driver working time limit constraints (MDGVRPTW-DTL). In ALNS-MDO, we used multiple deletion operators to destroy routes in each search cycle. At the same time, the number of customer nodes deleted in each deletion operator operation in each search cycle was adaptively selected. Finally, an operator weight coefficient reset strategy was added to avoid premature convergence of the algorithm. We conducted a comparative experiment between ALNS-MDO and five state-of-the-art optimization algorithms. The experimental results show that ALNS-MDO achieves better optimization results in a large number of experimental instances, which proves that ALNS-MDO has extensive advantages in optimization ability. At the same time, the necessity of each algorithm improvement and the stability of algorithm optimization ability have also been proven.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101942"},"PeriodicalIF":8.2,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated harvest and distribution scheduling of fresh agricultural products for multiple farms using a Q-learning-based artificial bee colony algorithm with problem knowledge

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-20 DOI: 10.1016/j.swevo.2025.101957

Xiaomeng Ma , Xujin Pu , Yaping Fu , Yuan Wang

{"title":"Integrated harvest and distribution scheduling of fresh agricultural products for multiple farms using a Q-learning-based artificial bee colony algorithm with problem knowledge","authors":"Xiaomeng Ma , Xujin Pu , Yaping Fu , Yuan Wang","doi":"10.1016/j.swevo.2025.101957","DOIUrl":"10.1016/j.swevo.2025.101957","url":null,"abstract":"<div><div>Nowadays, a great many of farmers sell fresh agricultural products through direct online sales. In this context, the farm-to-door supply mode has emerged, playing a crucial role in reducing transportation cost and quality deterioration. This work addresses an integrated harvest and farm-to-door distribution scheduling problem involving multiple farms. First, a mixed integer programming model is formulated to minimize total operation cost and maximize customer satisfaction regarding product quality. Second, a Q-learning-based artificial bee colony algorithm with problem knowledge (Q-ABC-K) is developed in particular. The algorithm is featured with the following strategies: (i) a hybrid initialization method with two rules to generate a high-quality population; (ii) a crossover operation to prompt a collaborative search between the population and external archive at the employed bee phase; (iii) a Q-learning method to favorably select premium neighborhood structures at the onlooker bee phase; and (iv) a knowledge-based local search method to refine the nondominated solutions. Finally, a large number of comparison experiments are conducted on a set of test instances. Through observing and analyzing the experimental results, three conclusions are acquired as follows: (i) The design of Q-learning and knowledge-based local search methods plays a significant role in enhancing the performance of Q-ABC-K; (ii) Q-ABC-K performs better than four state-of-the-art approaches in dealing with the considered problem; and (iii) Q-ABC-K has an advantage over an exact solver CPLEX in solving small-scale cases.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101957"},"PeriodicalIF":8.2,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online payments fraud prediction using optimized genetic algorithm based feature extraction and modified loss with XG boost algorithm for classification

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-19 DOI: 10.1016/j.swevo.2025.101934

R. Lingeswari , S. Brindha

{"title":"Online payments fraud prediction using optimized genetic algorithm based feature extraction and modified loss with XG boost algorithm for classification","authors":"R. Lingeswari , S. Brindha","doi":"10.1016/j.swevo.2025.101934","DOIUrl":"10.1016/j.swevo.2025.101934","url":null,"abstract":"<div><div>Online payment fraud become a pressing concern in the digital age, necessitating robust predictive models to identify fraudulent transactions effectively. This research proposes a novel approach that leverages an Optimized Genetic Algorithm (GA) for feature extraction and a Modified Loss function in conjunction with the XGBoost algorithm for classification. The first step involves the application of a GA to optimize feature selection. Genetic algorithms mimic the process of natural selection, iteratively evolving a population of potential feature subsets to maximize the predictive power of the model. This optimization process helps identify the most relevant features for fraud detection, reducing dimensionality and enhancing model efficiency. Next, a Modified Loss function is introduced to the XGBoost algorithm. Traditional loss functions aim to minimize prediction errors, but they may not be directly suited for fraud detection, where the focus is on correctly classifying fraudulent transactions. The Modified Loss function is tailored to prioritize the identification of fraudulent cases, thus improving the model's ability to differentiate between legitimate and fraudulent limitations transactions. The proposed approach is evaluated using real-world online payment transaction datasets, and its performance is compared to traditional methods. Experimental results demonstrate the superiority of the optimized genetic algorithm-based feature extraction and the Modified Loss with XGBoost algorithm for classification in terms of fraud detection accuracy, precision, and recall. By improving the accuracy and efficiency of fraud detection systems, this methodology can help financial institutions and e-commerce platforms protect their customers from fraudulent activities while reducing false positives.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101934"},"PeriodicalIF":8.2,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A niching differential evolution with Hilbert curve for multimodal multi-objective optimization

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-18 DOI: 10.1016/j.swevo.2025.101952

Guosen Li , Wenfeng Li , Lijun He , Cong Gao

{"title":"A niching differential evolution with Hilbert curve for multimodal multi-objective optimization","authors":"Guosen Li , Wenfeng Li , Lijun He , Cong Gao","doi":"10.1016/j.swevo.2025.101952","DOIUrl":"10.1016/j.swevo.2025.101952","url":null,"abstract":"<div><div>Multimodal multi-objective optimization problems have a many-to-one relationship between the decision space and the objective space. That is, distinct solutions in the decision space share the same objective value. How to coordinate population convergence and diversity while locating multimodal solutions is a challenging research topic. Some evolutionary algorithms using niching techniques have been reported in the literature. These algorithms prefer to induce multiple niches based on population information. Owing to the impact of convergence-first principle, the population tends to gather in easier-to-search regions, making it tough to yield more dispersed solutions in different niches. To remedy this situation, this paper proposes a niching differential evolution with Hilbert curve. First, a neighborhood-driven reproduction method is presented based on Hilbert curve, which features a two-layer architecture to capture promising regions and identify multimodal solutions. Second, a convergence-based density indicator is designed as a selection criterion to distinguish between convergence solutions and diversity solutions in the decision space. Moreover, fifteen intricate multimodal multi-objective test functions are devised. The experiments are performed on a series of test functions and a map-based practical problem. Empirical results attest that the proposed algorithm is competitive in dealing with multimodality compared with ten multimodal multi-objective algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101952"},"PeriodicalIF":8.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0