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A Constrained Many-Objective Mobile Crowdsensing Task Allocation Method Considering Latent Workers

IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 4065-4077页

作者： Cai, Xingjuan Ji, Chen Zhao, Tianhao Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China

The concept of mobile crowd sensing (MCS) represents a powerful model within the Internet of Things. It perceives different phenomena in the urban environment by taking advantage of the mobility of individuals and the sensors embedded in smartphones. Existing MCS task allocation systems typically assume that workers are in an idle state without any current tasks. However, this overlooks workers who are already engaged in other tasks but are closer to the task location, leading to potential resource wastage. In this article, we refer to such workers as "the latent workers." To address this issue, we propose a novel model called the constrained many-objective MCS task allocation model considering latent workers (CMaO-MCSTA-LW). This model aims to find a set of compromise solutions under multiple uncontrollable objective environments. To effectively solve this model, we developed a new algorithm called the dual-population constrained many-objective evolutionary algorithm based on the easing strategy and the elite parent strategy (dCMaOEA-E-P), which builds upon the original dCMaOEA-RAE. Experimental results demonstrate that, compared to several other representative algorithms, the proposed algorithm shows superior performance across multiple perspectives, significantly improving task allocation efficiency.

关键词： Sensors Resource management Mobile computing evolutionary computation Costs Internet of Things Encoding Software Scheduling Schedules evolutionary algorithm (EA) latent workers many-objective optimization mobile crowdsensing task allocation

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Coloring Dynamic Graphs With a Similarity and Pool-Based evolutionary Algorithm

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IEEE ACCESS 2025年 13卷 38054-38075页

作者： Terci, Gizem Sungu Boz, Betul Gebze Tech Univ Inst Informat Technol TR-41400 Kocaeli Turkiye Marmara Univ Comp Engn Dept TR-34854 Istanbul Turkiye

The graph coloring problem is a well-known optimization challenge, particularly relevant in dynamic environments where the graph undergoes continuous changes over time. evolutionary algorithms, known for their adaptability and effectiveness in handling NP-hard problems, are well-suited for tackling the issues related to coloring dynamic graphs. In this paper, we present a novel Similarity and Pool-Based evolutionary Algorithm designed to address the graph coloring problem on dynamic graphs. Our approach employs a partition-based representation that adapts to dynamic graph changes while preserving valuable historical information. The algorithm introduces an innovative similarity and conflict-based crossover operator aimed at minimizing the number of colors used, alongside a local search method to enhance solution diversity. We evaluated the performance of the proposed algorithm against a well-known heuristic for the graph coloring problem and a genetic algorithm with a dynamic population across a diverse set of dynamic graphs. Experimental results demonstrate that our algorithm consistently outperforms these alternatives by reducing the number of colors required in the majority of test cases.

关键词： Heuristic algorithms Color evolutionary computation Social networking (online) Real-time systems Partitioning algorithms Adaptation models Metaheuristics Genetic operators Communication networks Dynamic graphs edge-dynamic graphs node-dynamic graphs graph coloring problem crossover operator evolutionary algorithms

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AIEA: An Asynchronous Influence-Based evolutionary Algorithm for Expensive Many-Objective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第2期55卷 786-799页

作者： Wei, Feng-Feng Chen, Wei-Neng Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Buildingand Urban Sci Guangzhou 510006 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 30071 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea

In expensive multi/many-objective optimization problems (EMOPs), the expensive objectives are generally accessed through different simulation tools, leading to different evaluation latencies and unbearable computational time for serial optimization. One promising approach to improve efficiency is to perform simulation and build surrogates separately for each objective in parallel. However, how to improve the model accuracy and select promising candidates without global information are big challenges. To alleviate these problems, this article proposes an asynchronous influence-based SAEA (AIEA) based on the client-server model. Each client approximates an objective and the server takes charge for evolution. To adaptively select promising candidates, the influence degree is introduced in candidate selection, which is calculated in the objective space to judge which candidate has more beneficial influence for evolution. With the selected candidate, the most-uncertain-first strategy is devised in objective selection for asynchronous evaluations and model improvement. To handle incomplete objective values, the nearest neighbor inheritance is adopted for unevaluated objectives. Comprehensive experiments compared with five surrogate-assisted EAs demonstrate the global optimization and scalability of AIEA.

关键词： Optimization computational modeling Predictive models Training data Data models Vectors Servers Program processors evolutionary computation Distributed databases Expensive optimization many-objective optimization problems parallel and distributed evolutionary computation surrogate-assisted evolutionary algorithm

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Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets

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IEEE TRANSACTIONS ON evolutionary computation 2025年第1期29卷 187-204页

作者： Stripinis, Linas Kudela, Jakub Paulavicius, Remigijus Vilnius Univ Inst Data Sci & Digital Technol LT-08663 Vilnius Lithuania Brno Univ Technol Inst Automat & Comp Sci Brno 61600 Czech Republic

This article addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of available options. Manual selection without comprehensive studies can lead to suboptimal or incorrect results. In order to address this issue, we carefully selected 25 promising and representative state-of-the-art algorithms from both aforementioned classes. The evaluation with up to the 20 dimensions and large evaluation budgets $(10<^>{5}{\times }n)$ was carried out in a significantly expanded and improved version of the DIRECTGOLib v2.0 library, which included ten distinct collections of primarily continuous test functions. The evaluation covered various aspects, such as solution quality, time complexity, and function evaluation usage. The rankings were determined using statistical tests and performance profiles. When it comes to the problems and algorithms examined in this study, EA4eig, EBOwithCMAR, APGSK-IMODE, 1-DTC-GL, OQNLP, and DIRMIN stand out as superior to other derivative-free solvers in terms of solution quality. While deterministic algorithms can locate reasonable solutions with comparatively fewer function evaluations, most stochastic algorithms require more extensive evaluation budgets to deliver comparable results. However, the performance of stochastic algorithms tends to excel in more complex and higher-dimensional problems. These research findings offer valuable insights for practitioners and researchers, enabling them to tackle diverse optimization problems effectively.

关键词： Optimization Partitioning algorithms evolutionary computation Benchmark testing Stochastic processes Linear programming Closed box Derivative-free global optimization deterministic algorithms evolutionary computation (EC) algorithms nature-inspired meta-heuristics numerical benchmarking

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Gradient-Guided Local Search for Large-Scale Hypervolume Subset Selection

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IEEE TRANSACTIONS ON evolutionary computation 2025年第2期29卷 519-533页

作者： Nan, Yang Shu, Tianye Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China

The use of an unbounded archive (UA) has attracted much attention in the filed of evolutionary multiobjective optimization (EMO) since a solution set selected from the UA is often better than the final population. The size of the UA is very large (e.g., 1 000 000) since it is unbounded and it stores all the examined nondominated solutions during the execution of an EMO algorithm. Thus, an algorithm which can efficiently select a high-quality subset from a large-scale candidate set (e.g., UA) is needed. In this article, we propose a gradient-guided local search hypervolume subset selection (GL-HSS) algorithm to efficiently select a high-quality subset from a large-scale candidate set. In each iteration of GL-HSS, the gradient of the hypervolume (HV) contribution of each selected solution is used to guide the local search. As a result, the proposed algorithm can quickly improve the HV of the selected subset. Experimental results show that, compared to the existing subset selection algorithms, the proposed GL-HSS algorithm can efficiently select high-quality subsets from various large-scale candidate sets.

关键词： Approximation algorithms Classification algorithms Search problems Optimization Vectors evolutionary computation Minimization Time complexity Size measurement Indexes evolutionary multiobjective optimization (EMO) hypervolume subset selection (HSS) local search (LS) many-objective optimization

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evolutionary Algorithm for the Traveling Salesman Problem With Innovative Encoding on Hybrid Quantum-Classical Machines

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IEEE ACCESS 2025年 13卷 54223-54239页

作者： Saini, Ravi Mani, Ashish Prasad, M. S. Bhattacharyya, Siddhartha Platos, Jan Amity Univ Dept Comp Sci & Engn ASET Noida 201301 Uttar Pradesh India Amity Univ Amity Innovat & Design Ctr Noida 201301 Uttar Pradesh India Amity Univ Amity Inst Space Sci & Technol Noida 201301 Uttar Pradesh India VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70800 Czech Republic

The Traveling Salesman Problem (TSP) is a widely studied NP-complete optimization challenge with significant theoretical and practical implications. This study proposes a hybrid quantum-classical framework using a Quantum-Inspired evolutionary Algorithm (QEA) with Sort Gray Binary Encoding to solve the TSP. The proposed method guarantees the generation of valid TSP tours by eliminating invalid solutions. It employs quantum superposition with intrinsic randomness to enhance computational efficiency and scalability. The framework was implemented on cloud-based NISQ platforms, including IBM Quantum and AWS Braket, demonstrating its practicality and effectiveness. Experimental evaluations revealed that the proposed framework successfully solved TSP instances with up to 15 cities, achieving superior performance compared to classical methods and showcasing its ability to scale under NISQ constraints. The results also highlight the potential of hybrid quantum-classical approaches to overcome hardware limitations in current quantum systems. This study establishes a robust hybrid methodology for solving combinatorial optimization problems. It also sets a benchmark for leveraging the capabilities of NISQ-era quantum devices in real-world applications, thereby providing a foundation for future research in hybrid quantum-classical optimization techniques.

关键词： Urban areas Quantum computing Logic gates Encoding evolutionary computation Traveling salesman problems Qubit Mathematical models Quantum system Hardware evolutionary algorithm gray encoding hybrid algorithms noisy intermediate-scale quantum quantum computing traveling salesman problem

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Single-stage filter-based local feature selection using an immune algorithm for high-dimensional microarray data

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APPLIED SOFT COMPUTING 2025年 172卷

作者： Wang, Yi Li, Wenshan Li, Tao Tian, Hao Hubei Univ Econ Sch Informat Engn Wuhan 430205 Peoples R China Chengdu Univ Informat Technol Sch Cybersecur Chengdu 610225 Peoples R China Sichuan Univ Coll Cybersecur Chengdu 610065 Peoples R China

Feature selection is crucial for disease classification and prognosis in high-dimensional microarray data, as it reduces dimensionality, enhances model accuracy, and improves computational efficiency. However, most existing methods rely on a global set of features and overlook the relationships between features and sample subspaces. To address this limitation, this study proposes a filter-based local feature selection method based on an immune algorithm (IA-FLFS). This method dynamically assigns a unique feature subset to each sample neighborhood, replacing reliance on a global feature subset. It incorporates three key innovations: (1) a single- stage, filter-based approach that considers feature interactions and is independent of any learning model, ensuring high effectiveness and efficiency for high-dimensional datasets;(2) an enhanced clonal selection algorithm is utilized to identify feature subsets, enhancing search capabilities through filter-based initialization, adaptive differential evolution-based mutation, and symmetric uncertainty-based local search. (3) thread-level parallelism is applied to each feature subset, significantly reducing computation time. Experimental results on twelve datasets demonstrate IA-FLFS's superior accuracy, efficiency, and ability to produce smaller feature subsets, outperforming fourteen state-of-the-art feature selection methods on most datasets. Notably, compared to other local feature selection algorithms, it achieves significant accuracy improvements on over eight datasets, highlighting its potential as a powerful and efficient tool for high-dimensional microarray analysis.

关键词： Local feature selection Immune algorithm evolutionary computation Gene selection

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A Two-Individual evolutionary Algorithm for Cumulative Capacitated Vehicle Routing With Single and Multiple Depots

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IEEE TRANSACTIONS ON evolutionary computation 2025年第2期29卷 505-518页

作者： Zou, Yuji Hao, Jin-Kao Wu, Qinghua Univ Angers Dept Comp Sci LERIA F-49045 Angers France Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

The cumulative capacitated vehicle routing problem (CCVRP) with single or multiple depots (MDCCVRP) is a variant of the popular capacitated vehicle routing problem. Instead of minimizing the total travel time, the objective here is to minimize the sum of all customers' waiting times. This problem has a variety of real-world applications, especially in post-disaster humanitarian relief. To solve the challenging CCVRP and MDCCVRP, we propose a unified two-individual evolutionary algorithm that follows the memetic search framework. The algorithm integrates several key features: a two-individual population mechanism to accelerate convergence while maintaining population diversity, a dedicated edge assembly crossover to generate high-quality offspring, and an adaptive feasible and infeasible local search to achieve a balanced exploration between feasible and infeasible solutions. The algorithm is evaluated on 39 CCVRP instances and 78 MDCCVRP instances commonly used in the literature. computational results show that for the CCVRP, the algorithm outperforms the leading algorithms by achieving improved best results (new upper bound) for 13 instances and matching the best results for 23 other instances. For the MDCCVRP, the algorithm achieves nine improved best results and matches the best results for the remaining instances. The critical components of the algorithm are investigated to understand their contributions.

关键词： evolutionary computation Vehicle routing Statistics Sociology Routing Search problems Memetics Combinatorial optimization cumulative capacitated routing evolutionary algorithm heuristics vehicle routing

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A Comprehensive System to Support Decision Making in Highly Complex Project Portfolio Situations

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IEEE ACCESS 2025年 13卷 38115-38132页

作者： Solares, Efrain Fernandez, Eduardo Coello Coello, Carlos A. Segura Lozano, Xochitl Moreno-Cepeda, Reimundo Diaz, Raymundo Univ Autonoma Coahuila Fac Contaduria & Adm Torreon 27000 Mexico Ctr Invest & Estudios Avanzados IPN Departmento Comp Mexico City 07360 Mexico Univ Autonoma Coahuila Fac Ciencias Adm Saltillo 25225 Mexico Univ Autonoma Coahuila Fac Contaduria & Adm Saltillo 25280 Mexico Tecnol Monterrey Monterrey 64849 Mexico

This paper describes a new approach in project portfolio selection (PPS) problems, emphasizing the need to overcome traditional deficiencies with respect to multicriteria decision-making and multiobjective optimization. While existing methods typically allow the solving of partial aspects of the PPS problem, the proposed approach seeks to provide a holistic framework dealing with aspects like interdependence between projects, interaction among criteria, the incorporation of both cardinal and ordinal information, and a hierarchical multiobjective optimization. Unlike approaches that optimize portfolios neglecting superiority of some projects, or those that only assess individual projects without considering overall portfolio performance, the proposal allows for a compromise between both objectives. A case study is given, proving the application of the proposal for developing well-balanced portfolios aligned with strategic organizational goals and stakeholder preferences. The results point to significant improvements in the efficiency and effectiveness of decision-making, especially in complex project environments. This research contributes not only to the advancement of the theoretical framework of PPS but also to practical implications for portfolio management in a wide variety of organizational contexts.

关键词： Portfolios Proposals Optimization MCDM evolutionary computation Vectors Stakeholders Object recognition Costs Benchmark testing Decision making evolutionary algorithms multicriteria analysis multiobjective optimization outranking methods project portfolio selection

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An efficient PSO-based evolutionary model for closed high-utility itemset mining

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APPLIED INTELLIGENCE 2025年第4期55卷 1-17页

作者： Carstensen, Simen Lin, Jerry Chun-Wei Univ Bergen Bergen Norway Western Norway Univ Appl Sci Bergen Norway

High-utility itemset mining (HUIM) is a widely adopted data mining technique for discovering valuable patterns in transactional databases. Although HUIM can provide useful knowledge in various types of data, it can be challenging to interpret the results when many patterns are found. To alleviate this, closed high-utility itemset mining (CHUIM) has been suggested, which provides users with a more concise and meaningful set of solutions. However, CHUIM is a computationally demanding task, and current approaches can require prolonged runtimes. This paper aims to solve this problem and proposes a meta-heuristic model based on particle swarm optimization (PSO) to discover CHUIs, called CHUI-PSO. Moreover, the algorithm incorporates several new strategies to reduce the computational cost associated with similar existing techniques. First, we introduce Extended TWU pruning (ETP), which aims to decrease the number of possible candidates to improve the discovery of solutions in large search spaces. Second, we propose two new utility upper bounds, used to estimate itemset utilities and bypass expensive candidate evaluations. Finally, to increase population diversity and prevent redundant computations, we suggest a structure called ExploredSet to maintain and utilize the evaluated candidates. Extensive experimental results show that CHUI-PSO outperforms the current state-of-the-art algorithms regarding execution time, accuracy, and convergence.

关键词： evolutionary computation Closed high-utility itemset Data mining Optimization Particle swarm optimization

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