Swarm and Evolutionary Computation最新文献

{"title":"Quantum circuit mapping based on discrete particle swarm optimization and deep reinforcement learning","authors":"Yang-Zhi Li ,&nbsp;Wen Liu ,&nbsp;Guo-Sheng Xu ,&nbsp;Mao-Duo Li ,&nbsp;Kai Chen ,&nbsp;Shou-Li He","doi":"10.1016/j.swevo.2025.101923","DOIUrl":"10.1016/j.swevo.2025.101923","url":null,"abstract":"<div><div>Current quantum devices are constrained by a limited number of physical qubits and their sparse connectivity. When executing logical quantum circuits on these devices, it is necessary to map them into equivalent circuits adhering to these constraints. The initial allocation of logical qubits to physical ones and the strategic addition of SWAP gates to meet connectivity requirements are critical decisions impacting the performance of mapped circuits. To tackle these challenges, this paper presents a novel quantum circuit mapping method that integrates discrete particle swarm optimization and deep reinforcement learning. Initially, a qubit allocation algorithm using discrete particle swarm optimization with a sorting selection strategy quickly maps logical to physical qubits. Then, an enhanced double deep-Q-network based quantum gate scheduling algorithm with an action space search strategy obtains a SWAP addition scheme that results in shallower depth and fewer additional quantum gates. Comparisons on benchmark datasets (B131 and B114) and the IBM Q20 quantum device show that our method outperforms others regarding algorithm runtime, mapped circuit depth, and the number of added SWAP gates. It also demonstrates scalability on large-scale circuits and IBM Q127. Compared to heuristic methods (subgraph isomorphism and filtered depth-limited search based quantum circuit mapping algorithms), it reduces the average number of added SWAP gates by 29.1% and the average runtime by 41.95%. Compared to a recent reinforcement learning method using Monte Carlo Tree Search for mapping, it decreases the average added depth by 26.05% and the average number of added SWAP gates by 25.58%. Moreover, compared to commercial compilers tket and qiskit, the proposed method results in 14.73% and 16.55% fewer SWAP gates, respectively.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101923"},"PeriodicalIF":8.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740073","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}

{"title":"A single-objective Sequential Search Assistance-based Multi-Objective Algorithm Framework","authors":"Peng Chen ,&nbsp;Jing Liang ,&nbsp;Kangjia Qiao ,&nbsp;Xuanxuan Ban ,&nbsp;P.N. Suganthan ,&nbsp;Hongyu Lin ,&nbsp;Jilong Zhang","doi":"10.1016/j.swevo.2025.101916","DOIUrl":"10.1016/j.swevo.2025.101916","url":null,"abstract":"<div><div>In recent years, multi-objective optimization has garnered significant attention from researchers. Evolutionary algorithms are proven to be highly effective in solving complex optimization problems in plenty of cases. However, in the pursuit of improved performance, the focus on generality and efficiency has gradually been sidelined. To address this problem, this paper proposes a generalized framework, called Single-objective Sequential Search Assistance-based Multi-objective Algorithm Framework (SSMAF), to enhance the efficiency of existing multi-objective algorithms while reducing computational costs. The framework comprises two phases. The first phase involves two mechanisms to expedite the convergence of the population: (1) A Sequential Search Mechanism (SSM) is utilized to sequentially search corner solutions to enhance the quality of final population, which includes a corner solution search step and a standard solution detection step to search the Pareto Front (PF) while avoiding obtaining unexpected solutions; (2) A Diversity Search Method (DSM) is designed to conduct reinforced searches within localized regions and assess the population’s crowding degree to prevent it from getting stuck in local optima. After obtaining a population with better distribution, the existing multi-objective algorithms can regard it as the initial population to further search the PF. In the experiments, SSMAF is compared with 13 existing algorithms on 42 widely used benchmark test problems and 4 real-world problems. The experimental results show that SSMAF simultaneously improves the solution quality of existing algorithms while reducing their computational complexity.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101916"},"PeriodicalIF":8.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714599","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}

{"title":"OS-BiTP: Objective sorting-informed bidomain-information transfer prediction for dynamic multiobjective optimization","authors":"Shijie Zhao ,&nbsp;Tianran Zhang ,&nbsp;Lei Zhang ,&nbsp;Jinling Song","doi":"10.1016/j.swevo.2025.101918","DOIUrl":"10.1016/j.swevo.2025.101918","url":null,"abstract":"<div><div>Prediction response mechanisms based on transfer learning are extensively prevalent in dynamic multiobjective optimization algorithms (DMOAs), which transform historical information into a new environment for tracking the Pareto set (PS) or front (PF). However, many existing methods learn information of overall changes from old to new populations for prediction. Due to the different characteristics of individual variation within the population, this inevitably causes the valid information of more relevant individuals to be partially weakened during the training process, thus reducing transfer prediction-based accuracy. Therefore, this paper proposes an objective sorting-informed bidomain-information transfer prediction (OS-BiTP) for the DMOA based on individual objective variation, with the aim of transferring individuals within the same characteristics. The three core components in OS-BiTP are variation-based objective sorting (VOS), bidomain-information transfer within objective space (BiTOS), and bidomain-information transfer within decision space (BiTDS). Specifically, VOS divides the current PF into high- and low-objective variation classes and designs a modified linear prediction mechanism to forecast new environmental objective vectors. Afterward, VOS trains an easy transfer learning model to match old and new environmental individuals with the same objective variation classes to increase the transfer efficiency of individuals. To accurately track dynamic PFs and PSs, BiTOS and BiTDS perform intraclass correlation alignment for the same class of objective vectors and nondominated solutions and fine-tune the predicted objective vectors and solutions based on their variation differences. The numerical results demonstrate the superior performance and application of OS-BiTP via a systematic comparison with seven state-of-the-art DMOAs.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101918"},"PeriodicalIF":8.2,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714594","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}

{"title":"Decomposition-based dual-population evolutionary algorithm for constrained multi-objective problem","authors":"Yufeng Wang ,&nbsp;Yong Zhang ,&nbsp;Chunyu Xu ,&nbsp;Wen Bai ,&nbsp;Ke Zheng ,&nbsp;Wenyong Dong","doi":"10.1016/j.swevo.2025.101912","DOIUrl":"10.1016/j.swevo.2025.101912","url":null,"abstract":"<div><div>Constrained multi-objective optimization problems require optimizing and solving multiple objectives while satisfying the constraints. However, in the process of solving this problem, some constraints created infeasible obstacle regions, which led to the neglect of a portion of the constrained Pareto front (CPF). In order to solve this problem, A novel decomposition-based dual-population constrained multi-objective evolutionary algorithm (DD-CMOEA) is proposed. DD-CMOEA adopts a dual population collaborative search strategy, which can quickly find CPF. In the first stage, DD-CMOEA conducts dual population searches on CPF and unconstrained Pareto front (UPF) separately. During the search process, sub-population A uses unconstrained global exploration to obtain information that helps sub-population B jump through infeasible obstacle areas. In the second stage, when the convergence of the sub-population searching for UPF stagnates, the angle-based constraint advantage principle is used for reverse search. It ensures that the searched CPF solution set can be evenly distributed throughout the entire search space. The experimental results on three standard benchmark function suites show that DD-CMOEA outperforms the other six state-of-the-art algorithms in solving constrained multi-objective optimization problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101912"},"PeriodicalIF":8.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697061","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}

{"title":"Particle swarm optimization algorithm based on comprehensive scoring framework for high-dimensional feature selection","authors":"Bo Wei ,&nbsp;Shanshan Yang ,&nbsp;Wentao Zha ,&nbsp;Li Deng ,&nbsp;Jiangyi Huang ,&nbsp;Xiaohui Su ,&nbsp;Feng Wang","doi":"10.1016/j.swevo.2025.101915","DOIUrl":"10.1016/j.swevo.2025.101915","url":null,"abstract":"<div><div>Feature selection (FS) plays an important role in data preprocessing. However, with the ever-increasing dimensionality of the dataset, most FS methods based on evolutionary computational (EC) face the challenge of “the dimensionality curse”. To address this challenge, we propose an new particle swarm optimization algorithm based on comprehensive scoring framework (PSO-CSM) for high-dimensional feature selection. First, a piecewise initialization strategy based on feature importance is used to initialize the population, which can help to obtain a diversity population and eliminate some redundant features. Then, a comprehensive scoring mechanism is proposed for screening important features. In this mechanism, a scaling adjustment factor is set to adjust the size of the feature space automatically. As the population continues to evolve, its feature space is further reduced so as to focus on the more promising area. Finally, a general comprehensive scoring framework (CSM) is designed to improve the performance of EC methods in FS task. The proposed PSO-CSM is compared with 10 representative FS algorithms on 18 datasets. The experimental results show that PSO-CSM is highly competitive in solving high-dimensional FS problems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101915"},"PeriodicalIF":8.2,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687667","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}

{"title":"Swarm-intelligence-based value iteration for optimal regulation of continuous-time nonlinear systems","authors":"Ding Wang,&nbsp;Qinna Hu,&nbsp;Ao Liu,&nbsp;Junfei Qiao","doi":"10.1016/j.swevo.2025.101913","DOIUrl":"10.1016/j.swevo.2025.101913","url":null,"abstract":"<div><div>In this article, a swarm-intelligence-based value iteration (VI) algorithm is constructed to resolve the optimal control issue for continuous-time (CT) nonlinear systems. By leveraging the evolutionary concept of particle swarm optimization (PSO), the challenge of gradient vanishing is effectively overcome compared to traditional adaptive dynamic programming (ADP). Specifically, a PSO-based action network is implemented to perform policy improvement, eliminating the reliance on gradient information. Furthermore, within the ADP framework, the swarm-intelligence-based VI algorithm for CT systems is developed to address the challenges associated with constraints of initial admissible conditions and the difficulty of selecting probing signals in the traditional policy iteration method. The theoretical analysis is provided to show the convergence of the developed VI algorithm and the stability of the closed-loop system, respectively. Finally, under affine and non-affine backgrounds, two simulations are conducted to demonstrate the effectiveness and optimality of the established swarm-intelligence-based VI scheme for CT systems.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101913"},"PeriodicalIF":8.2,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687666","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}

{"title":"Optimizing MapReduce efficiency and reducing complexity with enhanced particle Swarm Optimization (MR-MPSO)","authors":"Chander Diwaker ,&nbsp;Vijay Hasanpuri ,&nbsp;Yonis Gulzar ,&nbsp;Bhanu Sharma","doi":"10.1016/j.swevo.2025.101917","DOIUrl":"10.1016/j.swevo.2025.101917","url":null,"abstract":"<div><div>Big data's explosive growth poses serious data management difficulties, especially given the data's unequal distribution across massive databases. Because of this mismatch, traditional software systems are less effective, which leads to complex and wasteful data processing. We provide MapReduce-Modified Particle Swarm Optimization (MR-MPSO), a unique optimization technique, to tackle this problem. This strategy not only improves the administration of enormous datasets but also tackles the complexity issue of data imbalance. The MR framework is used to handle large-scale data processing tasks, with MR-MPSO driving the map and reducing functions. Our technique combines adaptive inertia weight with Particle Swarm Optimization (PSO) to improve the accuracy and efficiency of optimization for 10 unimodal and multimodal benchmark functions. MR-MPSO outperforms four optimization algorithms—MR K-means, MR bat, MR whale, and regular MR-on measures such as fitness value mean, median, and standard deviation. Furthermore, MR-MPSO regularly enhances throughput and average I/O rate, especially in complex write operations, with gains ranging from 1.4 % to 28.9 % in throughput and 2.1 % to 17.7 % in I/O rate over typical MR approaches.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101917"},"PeriodicalIF":8.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687668","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}

{"title":"A cooperative discrete artificial bee colony algorithm with Q-learning for solving the distributed permutation flowshop group scheduling problem with preventive maintenance","authors":"Wan-Zhong Wu ,&nbsp;Hong-Yan Sang ,&nbsp;Quan Ke Pan ,&nbsp;Qiu-Yang Han ,&nbsp;Heng-Wei Guo","doi":"10.1016/j.swevo.2025.101910","DOIUrl":"10.1016/j.swevo.2025.101910","url":null,"abstract":"<div><div>With the rapid development of manufacturing technology, the multi-factory production considering group-based job processing and machine maintenance is being given increased focus, due to its potential for enhancing cost efficiency and productivity. Group constraints and machine maintenance play a critical role in modern manufacturing by reducing machine downtime, balancing production loads, and extending equipment lifespan. This paper studies the distributed permutation flowshop group scheduling problem with preventive maintenance (DPFGSP/PM) by proposing a cooperative discrete artificial bee colony (CDABC) algorithm, which is based on cooperative strategy, with the objective of minimizing the total flow time (TFT). A novel heuristic based on the group scheduling principles and TFT optimization is introduced in the initialization phase. In the evolutionary phase, the decomposition strategy and the Q-learning strategy are applied to evolve the populations of jobs and groups. Subsequently, these populations are merged to construct the complete solution, and the evaluation criterion is used to determine whether to expand the solution space. Extensive computational experiments and comparisons with state-of-the-art algorithms demonstrate that the proposed CDABC algorithm is an effective solution for DPFGSP/PM.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101910"},"PeriodicalIF":8.2,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644309","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}

{"title":"An efficient history-guided surrogate models-assisted niching evolutionary algorithm for expensive multimodal optimization","authors":"Ting Huang ,&nbsp;Bing-Bing Niu ,&nbsp;Yue-Jiao Gong ,&nbsp;Jing Liu","doi":"10.1016/j.swevo.2025.101906","DOIUrl":"10.1016/j.swevo.2025.101906","url":null,"abstract":"<div><div>This work addresses the challenge of multimodal optimization, aiming to identify multiple optimal solutions in costly and time-consuming evaluation scenarios, known as expensive multimodal optimization problems (EMMOPs). Existing methods that adopt surrogate models to approximate costly evaluations with challenges, such as high costs of constructing training sets, inaccurate optima detection, and difficulties balancing exploration and exploitation in multimodal landscapes. To address these issues, we propose an efficient Binary Space Partitioning (BSP)-based surrogate models (SMs)-assisted niching evolutionary algorithm (NEA), termed BSP-SMs-NEA. The BSP tree provides a structured method for storing and retrieving historical information, enabling efficient construction of training sets for SMs. The SMs are then adaptively constructed and updated across niches to maintain high accuracy. Furthermore, BSP-SMs assist the NEA in selective evolution, optimizing resource utilization while balancing exploration and exploitation. Compared with 11 existing methods on EMMOP benchmark, BSP-SMs-NEA demonstrates superior performance, achieving the best precision on 80% of test functions, along with the top success rate and statistical results of the best fitness value across all test functions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101906"},"PeriodicalIF":8.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642537","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}

{"title":"Parrot optimization algorithm for improved multi-strategy fusion for feature optimization of data in medical and industrial field","authors":"Gaoxia Huang ,&nbsp;Jianan Wei ,&nbsp;Yage Yuan ,&nbsp;Haisong Huang ,&nbsp;Hualin Chen","doi":"10.1016/j.swevo.2025.101908","DOIUrl":"10.1016/j.swevo.2025.101908","url":null,"abstract":"<div><div>Feature selection is crucial in machine learning, data mining and pattern recognition, aiming at refining data features and improving model performance. Data features in the medical-industrial field are numerous and often contain redundant and irrelevant information, which affects model efficiency and generalization ability. Given that the superior performance of meta-heuristic algorithms in dealing with complex constrained problems has been demonstrated and many researchers have used them for feature selection to process data with better results than traditional methods, this study innovatively proposes an improved Multi-Strategy Fused Parrot Optimization Algorithm (MIPO) to optimize the feature selection process targeting the medical-industrial data. MIPO incorporates four core mechanisms: first, balanced and optimized foraging behavior to pinpoint key features; second, lens imaging reverse dwell behavior to strengthen local search; third, vertical and horizontal cross-communication behavior to promote population co-evolution; and fourth, memory behavior to intelligently guide the search direction. In addition, the pacifying behavior strategy is introduced to enhance the stability and robustness of the algorithm in complex space. To fully validate MIPO, this paper designs exhaustive experiments, including ablation experiments, experiments comparing with mainstream algorithms and comparisons with other feature selection methods, to demonstrate its superior performance in multiple dimensions. Based on the S/V transfer function, nine binary variants are constructed to cope with the challenge of diverse feature selection. The experimental results show that MIPO and its variants exhibit efficient, general and strong generalization capabilities on 23 medical-industrial datasets. Further, by combining KNN, SVM, and RF classifiers, MIPO significantly improves the model accuracy, with average improvement rates of 55.38%, 35.53%, and 49.59%, respectively, compared with the original parrot algorithm, and the optimal variant also performs well on all types of classifiers, with average improvement rates of 53.91%, 34.38%, and 49.94% for the optimal variant, proving the wide applicability of MIPO. In this study, the adaptability of MIPO and classifiers is deeply explored to provide scientific guidance and practical suggestions for practical applications, which not only promotes the technological progress in the field of feature selection, but also provides a powerful tool for data processing and analysis in the field of medical and industrial.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101908"},"PeriodicalIF":8.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644308","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}