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Generating and Adapting to Diverse Ad Hoc Partners in Hanabi

IEEE TRANSACTIONS ON GAMES 2023年第2期15卷 228-241页

作者： Canaan, Rodrigo Gao, Xianbo Togelius, Julian Nealen, Andy Menzel, Stefan Cal Poly State Univ San Luis Obispo CA 93407 USA NYU Tandon Sch Engn Brooklyn NY 11201 USA Univ Southern Calif Los Angeles CA 90007 USA Honda Res Inst Europe GmbH D-63073 Offenbach Germany

Hanabi is a cooperative game that brings the problem of modeling other players to the forefront. In this game, coordinated groups of players can leverage preestablished conventions to great effect. In this article, we focus on ad hoc settings with no previous coordination between partners. We introduce a "Bayesian Meta-Agent" that maintains a belief distribution over hypotheses of partner policies. The policies that serve as initial hypotheses are generated using MAP-Elites, to ensure behavioral diversity. We evaluate an "Adaptive" version of the agent, which selects a response policy based on the updated belief distribution and a "Generalist" version, which selects a response based on the uniform prior. In short episodes of ten games with a consistent partner, the "Adaptive" version outperforms the "Generalist" when the training and evaluation populations are the same. This presents a first step toward an agent that can model its partner and adapt within a time frame that is compatible with human interaction.

关键词： Learning (artificial intelligence) Naive Bayes methods computational and artificial intelligence evolutionary computation

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Nature inspired optimization algorithms: a comprehensive overview

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EVOLVING SYSTEMS 2023年第1期14卷 141-156页

作者： Kumar, Ankur Nadeem, Mohammad Banka, Haider Aligarh Muslim Univ Dept Comp Sci Aligarh 202002 Uttar Pradesh India Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

Nature performs complex tasks in a simple yet efficient way. Natural processes may seem straightforward from outside but are composed of several inherently complicated sub-processes. Inspired from nature, several Nature Inspired Optimization Algorithms (NIOAs) have been developed in recent years. The family of NIOAs is expanding rapidly. Therefore, the set of NIOAs became quite large and selecting an appropriate NIOA is a tedious job. Since each one of the algorithms offers something novel, the similarities and differences among them are necessary to be established so that the selection of an algorithm for a particular problem becomes relatively easy. Moreover, a problem needs to be mapped in a NIOA, requiring understanding of fundamental components of NIOAs. Tuning parameters and algorithm operators another important concern in NIOAs that need be addressed carefully for better performance of the algorithm. Our work distinguishes NIOAs on the basis of various criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. The purpose of present study is to analyze major concepts related to NIOAs such as fundamentals of NIOAs, comparison among them, advancements, etc. In order to explain the usage of components of NIOA, an illustrative example is also presented.

关键词： Soft computing evolutionary computation Optimization Nature inspired algorithm

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evolutionary PSO-based emergency monitoring geospatial edge service chain in the emergency communication network

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International Journal of Digital Earth 2023年第1期16卷 2797-2817页

作者： Sheng He Xicheng Tan Yanfei Zhong Meng Huang Zhiyuan Mei You Wan Huaming Wang School of Remote Sensing and Information Engineering Wuhan UniversityWuhanPeople’s Republic of China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China

Emergency communication networks play a vital role in disaster monitoring,transmission,and application during disaster emergency response(DER),however,the performance and stability of edge nodes in the emergency communication networks are often weak due to limited communication and computation *** weakness directly affects the quality,of service(Qos)of the geospatial edge service(GES)chains involved in emergency *** research predominantly addresses service compositions in stable environments,neglecting the aggregation of efficient and robust GES chains in emergency communication *** study proposes an evolutionary_particie swarm optimization(EPSO)-based emergency monitoring GES chain in an_emergency communication *** includes a GES chain model of emergency environment monitoring for tailing areas,as well as the designs of the particle chromosome encoding method,fitness evaluation model,and particle chromosome swarm update operators of the EPSO-based GES ***,the study conducts emergency environment monitoring experiments for tailing areas using the proposed *** results demonstrate that the proposed method significantly enhances the efficiency,stability,and reliability of emergency monitoring GEs chains in the emergency communication *** is crucial to providing fast and reliable services for DER during natural disasters.

关键词： Edge computing emergency communication emergency monitoring evolutionary computation geospatiai service chain Pso

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Review and empirical analysis of sparrow search algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第10期56卷 10867-10919页

作者： Yue, Yinggao Cao, Li Lu, Dongwan Hu, Zhongyi Xu, Minghai Wang, Shuxin Li, Bo Ding, Haihua Wenzhou Univ Technol Sch Intelligent Mfg & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Intelligent Informat Syst Inst Wenzhou 325035 Peoples R China Wenzhou Univ Technol Wenzhou Key Lab Intelligent Lifeline Protect & Eme Wenzhou 325035 Peoples R China

In recent years, swarm intelligence algorithms have received extensive attention and research. Swarm intelligence algorithms are a biological heuristic method, which is widely used in solving optimization problems. The traditional swarm intelligence algorithms provide new ideas and new ways to solve some practical problems, and they have made positive progress in fields such as combinatorial optimization, task scheduling, process control, engineering prediction, and image processing. In particular, the sparrow search algorithm is a new type of group intelligence optimization algorithm inspired by the group foraging behavior to perform local and global search by imitating the foraging and anti-predation behavior of sparrows. In view of the shortcomings of the original sparrow search algorithm, such as its easy fall into local optimum, slow convergence speed, and low convergence accuracy, scholars at home and abroad have improved the sparrow search algorithm and have made practical applications in various fields. Firstly, this paper introduces the basic principle of sparrow search algorithm, analyzes the factors affecting the performance of the algorithm, further proposes the improvement strategy of the algorithm, and performs function test comparison and performance analysis with particle swarm optimization algorithm, monarch butterfly algorithm, colony spider algorithm, and pigeon swarm optimization algorithm. After that, the application and development of the sparrow search algorithm in power grid load forecasting, image processing, path tracking, wireless sensor network routing performance optimization, wireless location, and fault diagnosis are described. Finally, combined with the performance characteristics and application direction of the sparrow search algorithm, the future research and development direction of the sparrow search algorithm is prospected.

关键词： Swarm intelligence evolutionary computation Bio-inspired algorithm Sparrow search algorithm Improvement strategy

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Multiple Populations for Multiple Objectives Framework With Bias Sorting for Many-Objective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2023年第5期27卷 1340-1354页

作者： Yang, Qi-Te Zhan, Zhi-Hui Kwong, Sam Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Zhejiang Normal Univ Jinhua 321004 Zhejiang Peoples R China Hanyang Univ Ansan 15588 South Korea

The convergence and diversity enhancement of multiobjective evolutionary algorithms (MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active topic in evolutionary computation. By considering the advantages of the multiple populations for multiple objectives (MPMO) framework in solving multiobjective optimization problems and even MaOPs, this article proposes an MPMO-based algorithm with a bias sorting (BS) method (termed MPMO-BS) for solving MaOPs to achieve both good convergence and diversity performance. For convergence, the BS method is applied to each population of the MPMO framework to enhance the role of nondominated sorting by biasedly paying more attention to the objective optimized by the corresponding population. This way, all the populations in the MPMO framework evolve together to promote the convergence performance on all objectives of the MaOP. For diversity, an elite learning strategy is adopted to generate locally mutated solutions, and a reference vector-based maintenance method is adopted to preserve diverse solutions. The performance of the proposed MPMO-BS algorithm is assessed on 29 widely used MaOP test problems and two real-world application problems. The experimental results show its high effectiveness and competitiveness when compared with seven state-of-the-art MOEAs for many-objective optimization.

关键词： Bias sorting (BS) coevolution evolutionary computation many-objective optimization problems (MaOPs) multiobjective evolutionary algorithm (MOEA)

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A Hybrid evolutionary Algorithm Using Two Solution Representations for Hybrid Flow-Shop Scheduling Problem

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第3期53卷 1752-1764页

作者： Fan, Jiaxin Li, Yingli Xie, Jin Zhang, Chunjiang Shen, Weiming Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

As an extension of the classical flow-shop scheduling problem, the hybrid flow-shop scheduling problem (HFSP) widely exists in large-scale industrial production systems and has been considered to be challenging for its complexity and flexibility. evolutionary algorithms based on encoding and heuristic decoding approaches are shown effective in solving the HFSP. However, frequently used encoding and decoding strategies can only search a limited area of the solution space, thus leading to unsatisfactory performance during the later period. In this article, a hybrid evolutionary algorithm (HEA) using two solution representations is proposed to solve the HFSP for makespan minimization. First, the proposed HEA searches the solution space by a permutation-based encoding representation and two heuristic decoding methods to find some promising areas. Afterward, a Tabu search (TS) procedure based on a disjunctive graph representation is introduced to expand the searching space for further optimization. Two classical neighborhood structures focusing on critical paths are extended to the problem-specific backward schedules to generate candidate solutions for the TS. The proposed HEA is tested on three public HFSP benchmark sets from the existing literature, including 567 instances in total, and is compared with some state-of-the-art algorithms. Extensive experimental results indicate that the proposed HEA performs much better than the other algorithms. Moreover, the proposed method finds new best solutions for 285 hard instances.

关键词： Search problems Job shop scheduling Encoding Heuristic algorithms evolutionary computation Decoding Space exploration Critical paths hybrid evolutionary algorithm (HEA) hybrid flow-shop scheduling neighborhood structures Tabu search (TS)

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Multistate-Constrained Multiobjective Differential Evolution Algorithm With Variable Neighborhood Strategy

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第7期53卷 4459-4472页

作者： Hou, Ying Wu, Yilin Han, Honggui Beijing Univ Technol Fac Informat Technol Engn Res Ctr Digital Commun Beijing Lab Urban Mass TransitMinist Educ Beijing 100124 Peoples R China Beijing Univ Technol Fac Informat Technol Engn Res Ctr Intelligent Percept & Autonomous Con Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

Multiobjective differential evolution (DE) algorithm (MODE) has been widely used in multiobjective optimization problems. However, due to the complex feasible regions, the optimization efficiency of MODE may decrease when solving constrained multiobjective problems. It is challenging to promote the evolution of population with few feasible solutions. In this article, a multistate-constrained MODE with variable neighborhood strategy (MSCMODE-VNS) is proposed to enhance the optimization effectiveness with complex feasible regions. First, a variable neighborhood DE strategy, based on a specially designed convergence indicator, is designed to accelerate the generation of feasible solutions. Second, a multistate population updating strategy with a comprehensive solution evaluation mechanism is devised to update the population of the next generation to improve the performance of solutions. Third, the convergence analysis, based on the probability theory, is derived to verify the effectiveness of the proposed MSCMODE-VNS algorithm. Finally, experimental results indicate that MSCMODE-VNS can achieve a satisfactory performance on three benchmark test suites and two real-world-constrained multiobjective problems.

关键词： Statistics Sociology Optimization Convergence evolutionary computation Vehicle routing Linear programming Comprehensive solution evaluation mechanism constrained multiobjective optimization evolutionary state multiobjective differential evolution (DE) algorithm (MODE) variable neighborhood DE strategy

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Neural Architecture Search Based on a Multi-Objective evolutionary Algorithm With Probability Stack

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IEEE TRANSACTIONS ON evolutionary computation 2023年第4期27卷 778-786页

作者： Xue, Yu Chen, Chen Slowik, Adam Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Koszalin Univ Technol Dept Elect & Comp Sci Koszalin PL-75453 Poland

With the emergence of deep neural networks, many research fields, such as image classification, object detection, speech recognition, natural language processing, machine translation, and automatic driving, have made major breakthroughs in technology and the research achievements have been successfully applied in many real-life applications. Combining evolutionary computation and neural architecture search (NAS) is an important approach to improve the performance of deep neural networks. Usually, the related researchers only focus on precision. Thus, the searched neural architectures always perform poorly in the other indexes such as time cost. In this article, a multi-objective evolutionary algorithm with a probability stack (MOEA-PS) is proposed for NAS, which considers the two objects of precision and time consumption. MOEA-PS uses an adjacency list to represent the internal structure of deep neural networks. Besides, a unique mechanism is introduced into the multi-objective genetic algorithm to guide the process of crossover and mutation when generating offspring. Furthermore, the structure blocks are stacked using a proxy model to generate deep neural networks. The results of the experiments on Cifar-10 and Cifar-100 demonstrate that the proposed algorithm has a similar error rate compared with the most advanced NAS algorithms, but the time cost is lower. Finally, the network structure searched on Cifar-10 is transferred directly to the ImageNet dataset, which can achieve 73.6% classification accuracy.

关键词： Deep learning evolutionary computation multiobjective optimization neural architecture search (NAS)

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evolutionary computation for Predicting Optimal Reaction Knockouts and Enzyme Modulation Strategies

Evolutionary Computation for Predicting Optimal Reaction Kno...

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IEEE Congress on evolutionary computation

作者： Evangelista, Pedro Rocha, Miguel Rocha, Isabel Univ Minho Ctr Biol Engn IBB 4710-057 Campus Gualtar Braga Portugal Univ Minho Comp Sci & Technol Ctr P-4710057 Braga Portugal

ISBN: (纸本)9781479904549;9781479904532

One of the main purposes of Metabolic Engineering is the quantitative prediction of cell behaviour under selected genetic modifications. These methods can then be used to support adequate strain optimization algorithms in a outer layer. The purpose of the present study is to explore methods in which dynamical models provide for phenotype simulation methods, that will be used as a basis for strain optimization algorithms to indicate enzyme under/over expression or deletion of a few reactions as to maximize the production of compounds with industrial interest. This work details the developed optimization algorithms, based on evolutionary computation approaches, to enhance the production of a target metabolite by finding an adequate set of reaction deletions or by changing the levels of expression of a set of enzymes. To properly evaluate the strains, the ratio of the flux value associated with the target metabolite divided by the wild-type counterpart was employed as a fitness function. The devised algorithms were applied to the maximization of Serine production by Escherichia coli, using a dynamic kinetic model of the central carbon metabolism. In this case study, the proposed algorithms reached a set of solutions with higher quality, as compared to the ones described in the literature using distinct optimization techniques.

关键词： biology evolutionary computation

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Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization

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COMPLEX & INTELLIGENT SYSTEMS 2023年第2期9卷 1211-1228页

作者： Du, Ke-Jing Li, Jian-Yu Wang, Hua Zhang, Jun Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Vic 8001 Australia South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problem (MO-MTOP) using evolutionary computation. However, most existing methods tend to directly treat the multiple multi-objective tasks as different problems and optimize them by different populations, which face the difficulty in designing good knowledge transferring strategy among the tasks/populations. Different from existing methods that suffer from the difficult knowledge transfer, this paper proposes to treat the MO-MTOP as a multi-objective multi-criteria optimization problem (MO-MCOP), so that the knowledge of all the tasks can be inherited in a same population to be fully utilized for solving the MO-MTOP more efficiently. To be specific, the fitness evaluation function of each task in the MO-MTOP is treated as an evaluation criterion in the corresponding MO-MCOP, and therefore, the MO-MCOP has multiple relevant evaluation criteria to help the individual selection and evolution in different evolutionary stages. Furthermore, a probability-based criterion selection strategy and an adaptive parameter learning method are also proposed to better select the fitness evaluation function as the criterion. By doing so, the algorithm can use suitable evaluation criteria from different tasks at different evolutionary stages to guide the individual selection and population evolution, so as to find out the Pareto optimal solutions of all tasks. By integrating the above, this paper develops a multi-objective multi-criteria evolutionary algorithm framework for solving MO-MTOP. To investigate the proposed algorithm, extensive experiments are conducted on widely used MO-MTOPs to compare with some state-of-the-art and well-performing algorithms, which have verified the great effectiveness and efficiency of the proposed algorithm. Therefore, treating MO-MTOP as MO-MCOP is a potential and promising direction for solving MO-MTOP.

关键词： Multi-objective optimization Multi-criteria optimization Multi-task optimization evolutionary algorithm evolutionary computation

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