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An efficient strategy for using multifactorial optimization to solve the clustered shortest path tree problem

APPLIED INTELLIGENCE 2020年第4期50卷 1233-1258页

作者： Thanh, Pham Dinh Binh, Huynh Thi Thanh Trung, Tran Ba Taybac Univ Fac Math Phys Informat Son La Vietnam Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

Arising from the need of all time for optimization of irrigation systems, distribution network and cable network, Clustered Shortest-Path Tree Problem (CluSPT) has been attracting a lot of attention and interest from the research community. On the other hand, the multifactorial evolutionary algorithm (MFEA) is one of the most recently exploited realms of evolutionary algorithms (EAs) and its performance in solving optimization problems has been very promising. Considering these characteristics, this paper describes a new approach using the MFEA for solving the CluSPT. The MFEA has two tasks: the goal of the first task is to determine the best tree (w.r.t. cost minimization) which envelops all vertices of the CluSPT while the goal of the second task is to find the fittest solution possible for the problem. The purpose of the second task is to find good materials for implicit genetic transfer process in MFEA to improve the quality of CluSPT. To apply this new algorithm, a decoding scheme for deriving individual solutions from the unified representation in the MFEA is also introduced in this paper. Furthermore, evolutionary operators such as population initialization, crossover and mutation operators are also proposed. These operators are applicable for constructing valid solution from both sparse and complete graph. Although the proposed algorithm is slightly complicated for implementation, it can enhance ability to explore and exploit the Unified Search Space (USS). To prove this increment in performance i.e, to assess the effectiveness of the proposed algorithm and methods, the authors implemented them on both Euclidean and Non-Euclidean instances. Experiment results show that the proposed MFEA outperformed existing heuristic algorithms in most of the test cases. The impact of the proposed MFEA was analyzed and a possible influential factor that may be useful for further study was also pointed out.

关键词： multifactorial evolutionary algorithm Clustered shortest-path tree problem evolutionary algorithms Genetic algorithm multifactorial optimization

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Multi-objective evolutionary multi-tasking algorithm using cross-dimensional and prediction-based knowledge transfer

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INFORMATION SCIENCES 2022年 586卷 540-562页

作者： Chen, Qunjian Ma, Xiaoliang Yu, Yanan Sun, Yiwen Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China BGI Shenzhen Shenzhen 518083 Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen Peoples R China

Transfer learning is an important research topic in machine learning and recently has been introduced into evolutionary computation to form evolutionary multi-task optimization (EMTO). EMTO focuses on tackling multiple optimization tasks simultaneously based on knowledge transfer and reuse. Many EMTO algorithms have been proposed mainly for single-objective optimization problems and achieved success in various fields, yet multi objective EMTO remains a big challenge. The existing multi-objective EMTO algorithms tend to suffer from issues like slow convergence and degraded performance on less correlated tasks. To alleviate these issues, this paper proposes a new multi-objective EMTO algorithm by introducing cross-dimensional variable search and prediction-based individual search for efficient knowledge transfer. The cross-dimensional variable search optimizes a decision variable using information collected from other variables. The prediction based individual search performs individual mapping where the offspring solutions and the corresponding parent solutions are symmetrized about the predicted population center to maintain the population diversity. The proposed algorithm is tested on benchmark problems and the experimental results demonstrate the effectiveness and efficiency of the algorithm.(c) 2021 Elsevier Inc. All rights reserved.

关键词： Multi-objective optimization Multi-objective multi-task optimization multifactorial evolutionary algorithm evolutionary multi-tasking

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System-in-package design using multi-task memetic learning and optimization

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MEMETIC COMPUTING 2022年第1期14卷 45-59页

作者： Dai, Weijing Wang, Zhenkun Xue, Ke Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

System-in-Package (SiP) is an advanced packaging technology and developing rapidly in semiconductor industry. Electronic modules of this package type are individual integrated systems for specific applications. Therefore, those modules are usually characterized by multiple encapsulated components and sophisticated internal structures. However, such complexity brings great challenges to package design. Traditional methods, like design of experiments, response surface analysis, are widely used in this field, but their effectiveness drops rapidly due to increasing complexity. In current scenarios, not only do the amount of design variables increases, but also the modules have diverse design tasks to satisfy. Thereby, package design for SiP modules is a multi-task optimization problem. To resolve this issue, we propose a multi-task memetic learning and optimization algorithm, in which multi-output Gaussian process model and multifactorial evolutionary algorithm are employed. In this work, knowledge transfer between different tasks is activated during both the surrogate modeling and model optimization procedures. Several variants of the proposed algorithm are tested, and their modeling accuracy and optimization efficiency were compared. This interdisciplinary study shows the benefits of the memetic knowledge transfer mechanism in improving modeling and optimizing efficacy in multi-task scenarios and presents a viable approach to achieve both automation and optimization for complicated packaging design.

关键词： System-in-packaging Optimal design Multi-task memetic learning and optimization multifactorial evolutionary algorithm Gaussian process

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Towards urban energy sustainability and resiliency through smart and effective renewable resource allocation

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SUSTAINABLE CITIES AND SOCIETY 2024年 107卷

作者： Cheng, Lijun Samah, Azurah Zhang, Yubo Jia, Zongwei Li, Fuzhong Univ Technol Malaysia Fac Comp Skudai Malaysia Shanxi Agr Univ Sch Software Taigu 030801 Shanxi Peoples R China Shanxi Agr Univ Sch Informat Sci & Engn Taigu 030801 Shanxi Peoples R China

Urban energy sustainability and resiliency are paramount in addressing the challenges of modern urbanization. This paper introduces a novel framework that seamlessly integrates smart agriculture practices with an effective renewable resource allocation strategy to optimize energy utilization in urban and suburban areas. The optimization models employ a multifactorial evolutionary algorithm, providing a dynamic and adaptive approach to fine-tune system parameters. The results, presented through numeric tables and colorful figures, showcase optimized agricultural production levels, renewable resource utilization, and the impact of specific factors and parameters on algorithmic performance. Notably, the study reveals the intricate balance achieved through the integration of smart agriculture and renewable energy, offering actionable insights for sustainable urban development. The visualizations not only enhance result comprehension but also serve as powerful tools for effective communication. This research contributes to the evolving discourse on urban energy sustainability, providing a systematic approach for future investigations at the intersection of agriculture, renewable energy, and optimization algorithms.

关键词： Urban energy sustainability Smart agriculture Renewable resource allocation multifactorial evolutionary algorithm Optimization models

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Multitask Augmented Random Search in deep reinforcement learning

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APPLIED SOFT COMPUTING 2024年 160卷

作者： Thanh, Le Tien Thang, Ta Bao Van Cuong, Le Binh, Huynh Thi Thanh Grad Univ Adv Studies Dept Informat SOKENDAI Tokyo Japan Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

Reinforcement Learning (RL) has gained significant popularity in recent years for its ability to solve complex control problems. However, most existing RL algorithms are designed to train policies for each environment in isolation, limiting their applicability to real -world scenarios with many related environments. Recently, many multitask optimization algorithms have been proposed and successfully applied to a wide range of optimization problems. However, existing studies mainly focus on solving multiple continuous function benchmarks and overlook the potential of tailoring towards RL. In this paper, we propose a simple multitask optimization algorithm called Multitask Augmented Random Search (MARS) that trains multiple RL agents together and exploits the performance surplus from highly correlated tasks. MARS is a modification of the simple random search Augmented Random Search (ARS) algorithm, which has been shown to outperform complicated methods in solving continuous control MuJoCo environments such as Soft Actor -Critic (SAC), Proximal Policy Optimization (PPO), and Trusted Region Policy Optimization (TRPO). The experimental results also demonstrate that our proposed algorithm is more consistent in solving different instances of MuJoCo benchmark than ARS, multifactorial Evolution algorithm (MFEA), and Adaptive MFEA RL (AMFEARL) within the same number of training episodes.

关键词： multifactorial evolutionary algorithm Deep reinforcement learning Augmented random search

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evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions

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COGNITIVE COMPUTATION 2022年第3期14卷 927-954页

作者： Osaba, Eneko Del Ser, Javier Martinez, Aritz D. Hussain, Amir Basque Res & Technol Alliance BRTA TECNALIA Ed 700 Derio 48160 Spain Univ Basque Country UPV EHU Bilbao 48013 Spain Edinburgh Napier Univ Edinburgh Midlothian Scotland

In this work, we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valuable knowledge. Additionally, the emerging paradigm of evolutionary multitasking tackles multitask optimization scenarios by using biologically inspired concepts drawn from swarm intelligence and evolutionary computation. The main purpose of this survey is to collect, organize, and critically examine the abundant literature published so far in evolutionary multitasking, with an emphasis on the methodological patterns followed when designing new algorithmic proposals in this area (namely, multifactorial optimization and multipopulation-based multitasking). We complement our critical analysis with an identification of challenges that remain open to date, along with promising research directions that can leverage the potential of biologically inspired algorithms for multitask optimization. Our discussions held throughout this manuscript are offered to the audience as a reference of the general trajectory followed by the community working in this field in recent times, as well as a self-contained entry point for newcomers and researchers interested to join this exciting research avenue.

关键词： Transfer optimization Multitasking optimization evolutionary multitasking multifactorial evolutionary algorithm Multi-population multitasking

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Contrastive variational auto-encoder driven convergence guidance in evolutionary multitasking

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APPLIED SOFT COMPUTING 2024年 163卷

作者： Wang, Ruilin Feng, Xiang Yu, Huiqun Shanghai Engn Res Ctr Smart Energy Shanghai 200237 Peoples R China

Knowledge transfer is at the core of the evolutionary Multitasking (EMT) problem, as it exploits the interaction of inter-task common knowledge to accelerate task convergence. However, existing research on EMT is generally based solely on the framework of evolutionary computation, with limited integration of deep learning models. Additionally, few studies have found that utilizing deep learning models to generate individuals for transfer and guide the evolutionary trajectory may promote better convergence of algorithms. To address this research gap, we introduce the MFEA-VC (multifactorial evolutionary-Variational Auto-Encoder and Contrastive Learning) algorithm. Individuals are categorized based on task-label and inputted into a VAE, with sampling along feature dimensions. The VAE effectively guides the population towards better search areas by learning the latent trends of the distribution and generating transferred individuals. Simultaneously, a new training objective based on contrastive learning is proposed. This objective regulates the similarity between individuals from the same and different task-label, finely controlling individual features in the latent space. This approach makes the generated superior individuals more interpretable. To verify the superiority of MFEA-VC, we conduct comprehensive empirical studies on multi-task single-objective scenarios. We also validate the effectiveness of our improved loss function through theoretical analysis. The results demonstrate that, compared to state-of-the-art multifactorial algorithms, our method significantly enhances the global search capability during the early evolution stages, achieves excellent convergence results, and exhibits strong adaptability to heterogeneous tasks.

关键词： multifactorial evolutionary algorithm Contrastive learning Variational auto-encoder Knowledge transfer

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Two levels approach based on multifactorial optimization to solve the clustered shortest path tree problem

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evolutionary INTELLIGENCE 2022年第1期15卷 185-213页

作者： Huynh Thi Thanh, Binh Pham Dinh, Thanh Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Taybac Univ Fac Nat Sci & Technol Son La Vietnam

The Clustered Shortest-Path Tree Problem (CluSPT) has a great meaning in theoretical research as well as a wide range of applications in everyday life, especially in the field of network optimization. Being able to solve the CluSPT will path the way for improving practical systems such as agricultural irrigation and product distribution. multifactorial evolutionary algorithm (MFEA) is a variant of evolutionary algorithm (EA) aiming at simultaneously solving multiple factorial tasks which can be diverse in types, dimensionalities and search spaces. The applications of MFEA have yet to be fully exploited, but the realm has recently attracted much interest from the research community, especially those who are working on evolutionary multitasking. Considering these characteristics, this paper describes a new approach using the MFEA for solving the CluSPT. The MFEA has two tasks: the first is solving the CluSPT problem and the second is solving a new problem which is decomposed from the CluSPT problem. The goal of the first task is finding the fittest solution as possible for the CluSPT while the goal of the second task is determining the best tree (w.r.t. cost minimization) which envelops all vertices of the problem. This paper also introduces mutation, crossover and population initialization operators for a proposed MFEA to solve the CluSPT. Each of these operators deals with individuals in two phases, one of which ensures that the resulted individual is a spanning tree and the other phase ensures that each of its clustered sub-graphs is also a spanning tree. As the MFEA had to deal with these two tasks, a decoding method was also created to allow communication between the tasks. To assess the effectiveness of the proposed algorithm and methods, the authors implemented them on both Euclidean and Non-Euclidean instances. Experiment results show that the proposed MFEA outperformed the existing MFEA algorithms on two-thirds instances with average improvement value of 47%.

关键词： multifactorial evolutionary algorithm Clustered shortest-path tree problem evolutionary algorithms Genetic algorithm multifactorial optimization

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evolutionary Constrained Multi-task Optimization: Benchmark Problems and Preliminary Results 22

Evolutionary Constrained Multi-task Optimization: Benchmark ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Li, Yanchi Gong, Wenyin Li, Shuijia Univ Geosci Sch Comp Sci China Wuhan Peoples R China

ISBN: (纸本)9781450392686

Multi-task optimization (MTO) aims to solve multiple tasks simultaneously. However, multi-task evolutionary algorithms (MTEAs) hardly consider the problem with constraints, while most optimization problems, in reality, are with constraints. This study presents a benchmark of constrained multi-task optimization problems (CMTOPs), modified from the CEC2017 competition on evolutionary multi-task optimization and CEC2017 competition on constrained real-parameter optimization. Moreover, this study attempts to solve CMTOPs by incorporating constraint handling techniques into MTEAs. Experimental results demonstrate the complexity of the proposed benchmark in the context of CMTOPs and present the requirements for handling constraints in MTEAs. Our preliminary exploration also reveals prospects for the development of evolutionary algorithms in the area of constrained multi-task optimization. The Matlab source code can be obtained from https://***/intLyc/CMTO-Benchmark.

关键词： Constrained multi-task optimization evolutionary multi-task optimization constrained optimization evolutionary transfer optimization multifactorial evolutionary algorithm

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multifactorial Differential Evolution with Opposition-based Learning for Multi-tasking Optimization

Multifactorial Differential Evolution with Opposition-based ...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Yu, Yanan Zhu, Anmin Zhu, Zexuan Lin, Qiuzhen Yin, Jian Ma, Xiaoliang Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China

ISBN: (纸本)9781728121536

Recently, multi-tasking optimization (MTO) has become a rising research topic in the field of evolutionary computation that has attracted increasing attention of academia. Comparing with single-objective optimization (SOO) and multi-objective optimization (MOO), MTO can solve different optimization tasks simultaneously by utilizing inter-task similarities and complementarities. Based on crossover operator, the classical multifactorial evolutionary algorithm (MFEA) transfers inter-task knowledge. To broaden the search region and accelerate the convergence, this paper integrates differential evolution (DE) and opposition-based learning (OBL) into MFEA and hence proposes MFEA/DE-OBL. The motivation of integrating DE and OBL is that they have different search neighborhoods and strong complementarity with simulated binary crossover (SBX) used in MFEA. Furthermore, integrating DE and OBL can help MFEA jump out of local optima. The effectiveness and efficiency of integrating DE and OBL into MFEA are experimentally studied on a set of benchmark problems with different degrees of similarities. Experimental results demonstrate that the proposed MFEA/ DE-OBL dramatically improves the performance compared with the MFEA.

关键词： multi-tasking optimization multifactorial evolutionary algorithm opposition-based learning differential evolution

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