检索结果-内蒙古大学图书馆

Epoch-based Application of Problem-Aware Operators in a Multiobjective Memetic Algorithm for Portfolio optimization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Durán, Feijoo Colomine Cotta, Carlos Fernández-Leiva, Antonio J. San Cristóbal Venezuela Dept. Lenguajes y Ciencias de la Computación ETSI Informática Universidad de Málaga Campus de Teatinos Málaga29071 Spain ITIS Software Universidad de Málaga Spain

We consider the issue of intensification/diversification balance in the context of a memetic algorithm for the multiobjective optimization of investment portfolios with cardinality constraints. We approach this issue in this work by considering the selective application of knowledge-augmented operators (local search and a memory of elite solutions) based on the search epoch in which the algorithm finds itself, hence alternating between unbiased search (guided uniquely by the built-in search mechanics of the algorithm) and focused search (intensified by the use of the problem-aware operators). These operators exploit Sharpe index (a measure of the relationship between return and risk) as a source of problem knowledge. We have conducted a sensibility analysis to determine in which phases of the search the application of these operators leads to better results. Our findings indicate that the resulting algorithm is quite robust in terms of parameterization from the point of view of this problem-specific indicator. Furthermore, it is shown that not only can other non-memetic counterparts be outperformed, but that there is a range of parameters in which the MA is also competitive when not better in terms of standard multiobjective performance indicators. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

High-dimensional efficient global optimization using both random and supervised embeddings

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Priem, Rémy Diouane, Youssef Bartoli, Nathalie Dubreuil, Sylvain Saves, Paul AIAA Journal

Bayesian optimization (BO) is one of the most powerful strategies to solvecomputationally expensive-to-evaluate blackbox optimization problems. However,BO methods are conventionally used for optimization problems of small dimensionbecause of the curse of dimensionality. In this paper, a high-dimensionnaloptimization method incorporating linear embedding subspaces of small dimensionis proposed to efficiently perform the optimization. An adaptive learningstrategy for these linear embeddings is carried out in conjunction with theoptimization. The resulting BO method, named efficient global optimizationcoupled with random and supervised embedding (EGORSE), combines in an adaptiveway both random and supervised linear embeddings. EGORSE has been compared tostate-of-the-art algorithms and tested on academic examples with a number ofdesign variables ranging from 10 to 600. The obtained results show the highpotential of EGORSE to solve high-dimensional blackbox optimization problems,in terms of both CPU time and the limited number of calls to the expensiveblackbox simulation. © 2025, CC BY-NC-ND.

关键词： optimization algorithms

A Centrality-Guided Modularity optimization Algorithm for Overlapping and Nested Community Detection in Opportunistic Networks 19th

学校读者我要写书评

暂无评论

A Centrality-Guided Modularity Optimization Algorithm for Ov...

19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Wang, Shoucheng Guo, Xulong Xu, Gang Inner Mongolia University Hohhot010021 China Inner Mongolia Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Hohhot010021 China

ISBN: (纸本)9789819623723

This paper introduces a centrality-guided modularity optimization algorithm for overlapping and nested community detection (CG-MONCD), aimed at addressing community structure identification in opportunistic networks. Characterized by data transmission through sporadic encounters between nodes without the need for stable communication links, the CG-MONCD algorithm constructs a weighted graph model by quantifying the frequency and duration of encounters, as well as energy consumption. Incorporating a centrality-based initial community construction strategy and a method for identifying overlapping nodes, it effectively detects overlapping and nested communities within the network. Experimental results demonstrate that this algorithm outperforms existing methods in community detection, modularity optimization, and intra-community edge density, accurately revealing the network's nested communities and hierarchical features, thereby enhancing communication efficiency and network stability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

METAFOR: A Hybrid Metaheuristics Software Framework for Single-Objective Continuous optimization Problems

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Camacho-Villalón, Christian Dorigo, Marco Stützle, Thomas Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Université Libre de Bruxelles Brussels Belgium

Hybrid metaheuristics are powerful techniques for solving difficult optimization problems that exploit the strengths of different approaches in a single implementation. For algorithm designers, however, creating hybrid metaheuristic implementations has become increasingly challenging due to the vast number of design options available in the literature and the fact that they often rely on their knowledge and intuition to come up with new algorithm designs. In this paper, we propose a modular metaheuristic software framework, called METAFOR, that can be coupled with an automatic algorithm configuration tool to automatically design hybrid metaheuristics. METAFOR is specifically designed to hybridize Particle Swarm optimization, Differential Evolution and Covariance Matrix Adaptation–Evolution Strategy, and includes a local search module that allows their execution to be interleaved with a subordinate local search. We use the configuration tool irace to automatically generate 17 different metaheuristic implementations and evaluate their performance on a diverse set of continuous optimization problems. Our results show that, across all the considered problem classes, automatically generated hybrid implementations are able to outperform configured single-approach implementations, while these latter offer advantages on specific classes of functions. We provide useful insights on the type of hybridization that works best for specific problem classes, the algorithm components that contribute to the performance of the algorithms, and the advantages and disadvantages of two well-known instance separation strategies, creating stratified training set using a fix percentage and leave-one-class-out cross-validation. © 2025, CC BY.

关键词： optimization algorithms

Modified FOX Optimizer for Solving optimization problems

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Hasan, Dler O. Mohammed, Hardi M. Abdul, Zrar Khalid Department of Computer Science College of Science Charmo University Chamchamal/Sulaimani Kurdistan Region46023 Iraq

The FOX optimizer, inspired by red fox hunting behavior, is a powerful algorithm for solving real-world and engineering problems. However, despite balancing exploration and exploitation, it can prematurely converge to local optima, as agent positions are updated solely based on the current best-known position, causing all agents to converge on one location. This study proposes the modified FOX optimizer (mFOX) to enhance exploration and balance exploration and exploitation in three steps. First, the Oppositional-Based Learning (OBL) strategy is used to improve the initial population. Second, control parameters are refined to achieve a better balance between exploration and exploitation. Third, a new update equation is introduced, allowing agents to adjust their positions relative to one another rather than relying solely on the best-known position. This approach improves exploration efficiency without adding complexity. The mFOX algorithm's performance is evaluated against 12 well-known algorithms on 23 classical benchmark functions, 10 CEC2019 functions, and 12 CEC2022 functions. It outperforms competitors in 74% of the classical benchmarks, 60% of the CEC2019 benchmarks, and 58% of the CEC2022 benchmarks. Additionally, mFOX effectively addresses four engineering problems. These results demonstrate mFOX's strong competitiveness in solving complex optimization tasks, including unimodal, constrained, and high-dimensional problems. © 2025, CC BY.

关键词： optimization algorithms

Pessimistic bilevel optimization approach for decision–focused learning

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Jiménez, Diego Pagnoncelli, Bernardo K. Yaman, Hande SKEMA Business School France Faculty of Economics and Business KU Leuven Belgium

The recent interest in contextual optimization problems, where randomness is associated with side information, has led to two primary strategies for formulation and solution. The first, estimate-then-optimize, separates the estimation of the problem’s parameters from the optimization process. The second, decision-focused optimization, integrates the optimization problem’s structure directly into the prediction procedure. In this work, we propose a pessimistic bilevel approach for solving general decision-focused formulations of combinatorial optimization problems. Our method solves an Ε-approximation of the pessimistic bilevel problem using a specialized cut generation algorithm. We benchmark its performance on the 0-1 knapsack problem against estimate-then-optimize and decision-focused methods, including the popular SPO+ approach. Computational experiments highlight the proposed method’s advantages, particularly in reducing out-of-sample *** Codes 90B99 Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Permutation Picture of Graph Combinatorial optimization Problems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Min, Yimeng Department of Computer Science Cornell University IthacaNY14850 United States

This paper proposes a framework that formulates a wide range of graph combinatorial optimization problems using permutation-based representations. These problems include the travelling salesman problem, maximum independent set, maximum cut, and various other related problems. This work potentially opens up new avenues for algorithm design in neural combinatorial optimization, bridging the gap between discrete and continuous optimization techniques. © 2024, CC BY.

关键词： optimization algorithms

Robust portfolio optimization for recommender systems considering uncertainty of estimated statistics

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Yanagi, Tomoya Ikeda, Shunnosuke Takano, Yuichi Graduate School of Science and Technology University of Tsukuba 1–1–1 Tennodai Ibaraki Tsukuba-shi305–8573 Japan

This paper is concerned with portfolio optimization models for creating high-quality lists of recommended items to balance the accuracy and diversity of recommendations. However, the statistics (i.e., expectation and covariance of ratings) required for mean–variance portfolio optimization are subject to inevitable estimation errors. To remedy this situation, we focus on robust optimization techniques that derive reliable solutions to uncertain optimization problems. Specifically, we propose a robust portfolio optimization model that copes with the uncertainty of estimated statistics based on the cardinality-based uncertainty sets. This robust portfolio optimization model can be reduced to a mixed-integer linear optimization problem, which can be solved exactly using mathematical optimization solvers. Experimental results using two publicly available rating datasets demonstrate that our method can improve not only the recommendation accuracy but also the diversity of recommendations compared with conventional mean–variance portfolio optimization models. Notably, our method has the potential to improve the recommendation quality of various rating prediction algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

COMPARATIVE ANALYSIS OF INDICATORS FOR MULTIOBJECTIVE DIVERSITY optimization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Pereverdieva, Ksenia Deutz, André Ezendam, Tessa Bäck, Thomas Hofmeyer, Hèrm Emmerich, Michael T.M. Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Dept. of Built Environment Eindhoven University of Technology Eindhoven Netherlands Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

Indicator-based (multiobjective) diversity optimization aims at finding a set of near (Pareto-)optimal solutions that maximizes a diversity indicator, where diversity is typically interpreted as the number of essentially different solutions. Whereas, in the first diversity-oriented evolutionary multiobjective optimization algorithm, the NOAH algorithm by Ulrich and Thiele, the Solow Polasky Diversity (also related to Magnitude) served as a metric, other diversity indicators might be considered, such as the parameter-free Max-Min Diversity, and the Riesz s-Energy, which features uniformly distributed solution sets. In this paper, focusing on multiobjective diversity optimization, we discuss different diversity indicators from the perspective of indicator-based evolutionary algorithms (IBEA) with multiple objectives. We examine theoretical, computational, and practical properties of these indicators, such as monotonicity in species, twinning, monotonicity in distance, strict monotonicity in distance, uniformity of maximizing point sets, computational effort for a set of size n, single-point contributions, subset selection, and submodularity. We present new theorems—including a proof of the NP-hardness of the Riesz s-Energy Subset Selection Problem—and consolidate existing results from the literature. In the second part, we apply these indicators in the NOAH algorithm and analyze search dynamics through an example. We examine how optimizing with one indicator affects the performance of others and propose NOAH adaptations specific to the Max-Min indicator. © 2024, CC BY.

关键词： optimization algorithms