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Evolution of path costs for efficient decentralized multi-agent pathfinding

SWARM AND evolutionary computation 2025年 93卷

作者： Farhadi, Ulrich Hess, Henning Maoudj, Abderraouf Christensen, Anders Lyhne Univ Southern Denmark SDU UAS Ctr MMMI Campusvej 55 DK-5230 Odense Denmark King Fahd Univ Petr & Minerals KFUPM Interdisciplinary Res Ctr Intelligent Mfg & Robot Dhahran Saudi Arabia

Efficient multi-agent pathfinding (MAPF) is becoming increasingly relevant in real-world scenarios. MAPF aims to minimize the travel time for robots operating in a shared environment. To date, substantial research has been dedicated to offline centralized algorithms based on conflict avoidance. Recently, an entirely decentralized algorithm, IDCMAPF, was introduced, premised on online conflict resolution. Decentralized approaches offer potential advantages in scalability, flexibility, and inherent robustness compared to centralized methods, as conflicts are handled locally. However, in scenarios with high-robot densities, decentralized conflict handling is not always effective. In this paper, we therefore propose a combination of online conflict resolution and evolved local path costs that promote conflict avoidance by discouraging paths through highly congested areas. We represent the environment as a directed graph and evolve local path costs. Our study examines two encodings: edge weight, an explicit encoding of all edge weights in the environment, and node vector, a compact encoding where each cell in the environment is assigned a two-dimensional vector. We conduct a comprehensive set of experiments to evaluate the performance of decentralized MAPF with evolved path costs and compare the results with state-of-the-art centralized algorithms on benchmark maps. Our findings reveal that edge weight encoding outperforms node vector encoding in complex, high-density scenarios. Conversely, the node vector encoding shows promise when specific environmental features, such as long corridors, are present. We also find that decentralized conflict handling, combined with evolved path costs, is effective and often yields performance comparable to state-of-the-art centralized planners.

关键词： MAPF Decentralized coordination Environmental optimization evolutionary computation

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MOTEA-II: A Collaborative Multiobjective Transformation-Based evolutionary Algorithm for Bilevel Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2025年第2期29卷 474-489页

作者： Chen, Lei Cheung, Yiu-Ming Liu, Hai-Lin Lai, Yutao Guangdong Univ Technol Sch Math & Stat Guangzhou 510006 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

evolutionary algorithms (EAs) for optimization have received wide attention due to their robustness and practicality. However, the traditional way of asynchronously handling bilevel optimization problems (BLOPs) ignores the benefits brought by effective upper- and lower-level collaboration. To address this issue, this article proposes a collaborative multiobjective transformation (MOT)-based EA (MOTEA-II). In MOTEA-II, the BLOP is handled within a decomposition-based multiobjective optimization paradigm using a two-stage collaborative MOT strategy. The stage-1 MOT focuses on multiple lower-level optimizations and collaboration, while stage-2 collaborates the upper-level optimization with lower-level optimization, which makes simultaneously horizontal and vertical optimization information sharing in bilevel optimization possible. In addition, a dynamic decomposition strategy is further proposed to reconstruct the hierarchy relationship in collaborative multiobjective optimization, facilitating the adaptive and flexible importance control of the upper-level objective optimization and lower-level optimality satisfaction for better-bilevel search efficiency. Empirical studies are conducted on two groups of commonly used BLOP benchmark suites and four practical applications. Experimental results show that the proposed collaborative MOTEA-II can achieve performance comparable to that of the previous MOTEA and three other representative EA-based bilevel optimization approaches, but using much fewer computational resources.

关键词： Optimization Collaboration Search problems evolutionary computation Metaheuristics Vectors Multitasking Linear programming computational efficiency Benchmark testing Collaborative multiobjective transformation dynamic decomposition evolutionary bilevel optimization

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A Novel Immune Algorithm for Multiparty Multiobjective Optimization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2025年第2期9卷 1238-1252页

作者： Chen, Kesheng Luo, Wenjian Zhou, Qi Liu, Yujiang Xu, Peilan Shi, Yuhui Harbin Inst Technol Sch Comp Sci & Technol Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen 518055 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Southern Univ Sci & Technol Sch Comp Sci & Engn Shenzhen 518055 Peoples R China

Traditional multiobjective optimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical applications. These scenarios are categorized as multiparty multiobjective optimization problems (MPMOPs). For MPMOPs, the goal is to find a solution set that is as close to the Pareto front of each DM as much as possible. This poses challenges for evolutionary algorithms in terms of searching and selecting. To better solve MPMOPs, this paper proposes a novel approach called the multiparty immune algorithm (MPIA). The MPIA incorporates an inter-party guided crossover strategy based on the individual's non-dominated sorting ranks from different DM perspectives and an adaptive activation strategy based on the proposed multiparty cover metric (MCM). These strategies enable MPIA to activate suitable individuals for the next operations, maintain population diversity from different DM perspectives, and enhance the algorithm's search capability. To evaluate the performance of MPIA, we compare it with ordinary multiobjective evolutionary algorithms (MOEAs) and state-of-the-art multiparty multiobjective optimization evolutionary algorithms (MPMOEAs) by solving synthetic multiparty multiobjective problems and real-world biparty multiobjective unmanned aerial vehicle path planning (BPUAV-PP) problems involving multiple DMs. Experimental results demonstrate that MPIA outperforms other algorithms.

关键词： Optimization Autonomous aerial vehicles Search problems Vectors Safety Minimization evolutionary computation Convergence Sorting Heuristic algorithms Multiparty multiobjective optimization evolutionary algorithm immune algorithm UAV path planning

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Evolving meta-correlation classes for binary similarity

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PATTERN RECOGNITION 2025年 157卷

作者： Franzoni, Valentina Biondi, Giulio Liu, Yang Milani, Alfredo Univ Perugia Dept Math & Comp Sci Via Vanvitelli 1 I-06123 Perugia Italy Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

In the field of machine learning and pattern recognition, the use of binary correlation indices is essential for accurate prediction and modelling. This work presents a novel evolutionary method to address the problem of discovering binary correlation indices in different application domains. The proposed approach introduces the concept of meta-correlation, a parametric formula representing classes of binary similarity indices, and optimizes it through an evolutionary scheme. The method has been experimented with and validated in the context of the link prediction problem based on local topological similarity (i.e. graph neighbourhood). A Differential Evolution optimization algorithm finds the evolved correlations that perform best in a given domain. Experiments conducted across different network domains have shown that the instances of the discovered meta-correlations generally outperform state-of-the-art binary correlation indices for all the experimented domains. This approach effectively explores the correlation space and can find a unique pattern that adapts to the domains under consideration. The meta-correlation classes can be applied to both topological and semantic similarity problems, taking into account local information without requiring complete knowledge of the graph.

关键词： evolutionary computation Network topology Complex networks Link prediction Binary similarity

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DetPy (Differential Evolution Tools): A Python toolbox for solving optimization problems using differential evolution

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SOFTWAREX 2025年 29卷

作者： Zielinski, Blazej Sciegienny, Szymon Orlicki, Hubert Ksiazek, Wojciech Cracow Univ Technol Fac Comp Sci & Telecommun Dept Comp Sci Krakow Poland

The differential evolution algorithm, introduced in 1997, remains one of the most frequently used methods for solving complex optimization problems. The basic version of the algorithm is widely available and implemented in many popular programming languages. However, the algorithm has continued to evolve, with newer, improved variants often achieving superior results over the original. Unfortunately, many of these modifications are not readily accessible as prebuilt programming solutions, creating a need for a comprehensive programming library that includes the most popular and effective variants of the base algorithm. The library we designed, DetPy (Differential Evolution Tools), provides implementations of the standard differential evolution algorithm along with 15 distinct variants. This tool allows researchers working on optimization problems to compare multiple algorithmic approaches, making it easier to select the most effective solution for their specific challenges.

关键词： evolutionary computation Genetic algorithm Differential evolution Optimization evolutionary algorithms Metaheuristics

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Development of a New Unit Commitment Method With Quantum Predator Prey Brain Storm Optimization

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ELECTRICAL ENGINEERING IN JAPAN 2025年

作者： Kawauchi, Yusuke Mori, Hiroyuki Chiang, Hsiao-Dong Meiji Univ Dept Network Design Nakano Ku Tokyo Japan Cornell Univ Dept Elect & Comp Engn Ithaca NY USA

This paper proposes a new method for unit commitment (UC) with Quantum Predator Prey Brain Storm Optimization (QPPBSO). The UC problems may be expressed as a mixed integer nonlinear programming problem in which binary variables mean on/off conditions of units and continuous ones imply their output. Recently, evolutionary computation (EC) has been applied to the UC problems due to the existence of indifferentiable cost functions such as large-scale steam turbine units, etc. However, there is still room for improvement in EC because the UC problems have high nonlinear features. This paper focuses on the integration of EC with Quantum Computing (QC) that is promising in power systems. Specifically, this paper combines QC with Predator Prey Brain Storm Optimization (PPBSO) of high-performance EC. The effectiveness of the proposed method is demonstrated in the New England 39-node system.

关键词： evolutionary computation Predator Prey BSO Quantum BSO quantum computing unit commitment

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Stochastic Fractal Search: A Decade Comprehensive Review on Its Theory, Variants, and Applications

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CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 2025年第3期142卷 2339-2404页

作者： El-Shorbagy, Mohammed A. Bouaouda, Anas Abualigah, Laith Hashim, Fatma A. Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Hassan II Univ Casablanca Fac Sci & Tech Mohammadia 28806 Morocco Sunway Univ Sch Engn & Technol Petaling Jaya 27500 Selangor Malaysia Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Helwan Univ Fac Engn Cairo 11792 Egypt Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan

With the rapid advancements in technology and science, optimization theory and algorithms have become increasingly important. A wide range of real-world problems is classified as optimization challenges, and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains, such as machine learning, process control, and engineering design, showcasing their capability to address complex optimization problems. The Stochastic Fractal Search (SFS) algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials. Since its introduction by Hamid Salimi in 2015, SFS has garnered significant attention from researchers and has been applied to diverse optimization problems across multiple disciplines. Its popularity can be attributed to several factors, including its simplicity, practical computational efficiency, ease of implementation, rapid convergence, high effectiveness, and ability to address single- and multi-objective optimization problems, often outperforming other established algorithms. This review paper offers a comprehensive and detailed analysis of the SFS algorithm, covering its standard version, modifications, hybridization, and multi-objective implementations. The paper also examines several SFS applications across diverse domains, including power and energy systems, image processing, machine learning, wireless sensor networks, environmental modeling, economics and finance, and numerous engineering challenges. Furthermore, the paper critically evaluates the SFS algorithm's performance, benchmarking its effectiveness against recently published meta-heuristic algorithms. In conclusion, the review highlights key findings and suggests potential directions for future developments and modifications of the SFS algorithm.

关键词： Meta-heuristic algorithms stochastic fractal search evolutionary computation engineering applications swarm intelligence optimization

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Decoupling Constraint: Task Clone-Based Multitasking Optimization for Constrained Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2025年第2期29卷 404-417页

作者： Li, Genghui Wang, Zhenkun Gao, Weifeng Wang, Ling Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Xidian Univ Sch Math & Stat Xian 710126 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The coupling of multiple constraints can pose difficulties in solving constrained multiobjective optimization problems (CMOPs). Existing constrained multiobjective evolutionary algorithms (CMOEAs) often overlook this issue by considering all constraints together. This article proposes MTOTC, a novel multitasking optimization algorithm that addresses this challenge through a task clone technique. MTOTC clones the target CMOP with q constraints into q+1 copies, resulting in a total of q+2 tasks. Each cloned task is handled using an independent population that considers a unique constraint-handling sequence, effectively decoupling the constraints in q+1 different ways. Additionally, the algorithm incorporates online information sharing between the target task and cloned tasks, enabling the utilization of valuable search history as much as possible. Experimental results on four recently developed complex CMOP benchmark suites and a series of real-world CMOPs demonstrate the superior performance of MTOTC compared to seven state-of-the-art CMOEAs.

关键词： Task analysis Optimization Statistics Sociology evolutionary computation Convergence Cloning Constrained multiobjective optimization constraint decoupling information transfer task clone

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Genetic Multi-Armed Bandits: A Reinforcement Learning Inspired Approach for Simulation Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2025年第2期29卷 360-374页

作者： Preil, Deniz Krapp, Michael Univ Augsburg Dept Quant Methods D-86159 Augsburg Germany EON Digital Technol D-30539 Hannover Germany

Many real-world problems are inherently stochastic, complicating, or even precluding the use of analytical methods. These problems are often characterized by high dimensionality, large solution spaces, and numerous local optima, which make finding optimal solutions challenging. Therefore, simulation optimization is frequently employed. This article specifically focuses on the discrete case, also known as discrete optimization via simulation. Despite their adaptions for stochastic problems, previous evolutionary algorithms face a major limitation in these problems. They discard all information about solutions that are not involved in the most recent population. However, this is ineffective, as each simulation observation gathered over the course of iterations provides valuable information that should guide the selection of subsequent solutions. Inspired by the domain of reinforcement learning (RL), we propose a novel memory concept for evolutionary algorithms that ensures global convergence and significantly improves their finite time performance. Unlike previous evolutionary algorithms, our approach permanently preserves simulation observations to progressively improve the accuracy of sample means when revisiting solutions in later iterations. Moreover, the selection of new solutions is based on the entire memory rather than just the last population. The numerical experiments demonstrate that this novel approach, which combines a genetic algorithm (GA) with such memory, consistently outperforms popular convergent state-of-the-art benchmark algorithms in a large variety of established test problems while requiring considerably less computational effort. This marks the so-called genetic multi-armed bandit (MAB) as one of the currently most powerful algorithms for solving stochastic problems.

关键词： evolutionary computation Convergence Optimization Genetics Resource management Complexity theory Search problems Program processors Genetic algorithms (GAs) multi-armed bandits (MABs) reinforcement learning (RL) simulation simulation optimization simulation simulation optimization

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A Subspace Search-Based evolutionary Algorithm for Large-Scale Constrained Multiobjective Optimization and Application

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第5期55卷 2486-2499页

作者： Ban, Xuanxuan Liang, Jing Yu, Kunjie Qiao, Kangjia Suganthan, Ponnuthurai Nagaratnam Wang, Yaonan Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Longmen Lab Luoyang 471000 Peoples R China Henan Inst Technol Sch Elect Engn & Automat Xinxiang 453002 Peoples R China Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar Hunan Univ Sch Elect & Informat Engn Changsha 410082 Peoples R China

Large-scale constrained multiobjective optimization problems (LSCMOPs) exist widely in science and technology. LSCMOPs pose great challenges to algorithms due to the need to optimize multiple conflicting objectives and satisfy multiple constraints in a large search space. To better address such problems, this article proposes a dynamic subspace search-based evolutionary algorithm for solving LSCMOPs. The main idea is to initially allow the population to search in a low-dimensional subspace to increase convergence, then the searched subspace is gradually expanded to encourage the population to further search the full decision space. Specifically, the contribution of each decision variable to the evolution is first calculated using the proposed decision variable analysis method. Then, a probability-based offspring generation strategy is developed to encourage the population to preferentially search in a low-dimensional subspace composed of decision variables with high contribution degrees, thus speeding up the early convergence. With the continuous progress of evolution, the subspace is gradually expanded to ensure that the population can better explore the entire space. The performance of the proposed algorithm is evaluated on a variety of test problems with 100-1000 decision variables. Experimental results on four test suits and three real-world instances show that the proposed algorithm is efficient in solving LSCMOPs.

关键词： Optimization Convergence Search problems Space exploration evolutionary computation Vectors Transforms Heuristic algorithms Couplings Research and development Contribution decision variables analysis large-scale constrained multiobjective optimization optimization probabilities subspace

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