Optimization by Hybridization of a Genetic Algorithm with the PROMOTHEE Method: Management of Multicriteria Localization

• URL: http://arxiv.org/abs/2002.04068v1
• Date: Fri, 10 Jan 2020 12:17:42 GMT
• Title: Optimization by Hybridization of a Genetic Algorithm with the PROMOTHEE Method: Management of Multicriteria Localization
• Authors: Myriem Alijo, Otman Abdoun, Mostafa Bachran, Amal Bergam
• Abstract summary: This work consists in hybridizing through genetic algorithms, economic intelligence (EI) and multicriteria analysis methods (MCA) The purpose is to lead the company to locate its activity in the place that would allow it a competitive advantage.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The decision to locate an economic activity of one or several countries is made taking into account numerous parameters and criteria. Several studies have been carried out in this field, but they generally use information in a reduced context. The majority are based solely on parameters, using traditional methods which often lead to unsatisfactory solutions.This work consists in hybridizing through genetic algorithms, economic intelligence (EI) and multicriteria analysis methods (MCA) to improve the decisions of territorial localization. The purpose is to lead the company to locate its activity in the place that would allow it a competitive advantage. This work also consists of identifying all the parameters that can influence the decision of the economic actors and equipping them with tools using all the national and international data available to lead to a mapping of countries, regions or departments favorable to the location. Throughout our research, we have as a goal the realization of a hybrid conceptual model of economic intelligence based on multicriteria on with genetic algorithms in order to optimize the decisions of localization, in this perspective we opted for the method of PROMETHEE (Preference Ranking Organization for Method of Enrichment Evaluation), which has made it possible to obtain the best compromise between the various visions and various points of view.

Related papers

• A New Approach for Multicriteria Assessment in the Ranking of Alternatives Using Cardinal and Ordinal Data [0.0]
  We propose a novel MCA approach that combines two Virtual Gap Analysis (VGA) models.<n>The VGA framework, rooted in linear programming, is pivotal in the MCA methodology.
  arXiv  Detail & Related papers  (2025-07-10T04:00:48Z)
• Decision Tree Induction Through LLMs via Semantically-Aware Evolution [53.0367886783772]
  We propose an evolutionary optimization method for decision tree induction based on genetic programming (GP) Our key innovation is the integration of semantic priors and domain-specific knowledge about the search space into the algorithm. This is operationalized through novel genetic operators that work with structured natural language prompts.
  arXiv  Detail & Related papers  (2025-03-18T12:52:03Z)
• Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation [6.980546503227467]
  We introduce a novel framework called generative curation, which optimize the true desirability of decision options by integrating both quantitative and qualitative aspects. We propose two implementation approaches: a generative neural network architecture that produces a distribution $pi$ to efficiently sample a diverse set of near-optimal actions, and a sequential optimization method to iteratively generate solutions. We validate our approach with extensive datasets, demonstrating its effectiveness in enhancing decision-making processes across a range of complex environments.
  arXiv  Detail & Related papers  (2024-09-17T20:13:32Z)
• Optimizing Feature Selection with Genetic Algorithms: A Review of Methods and Applications [4.395397502990339]
  Genetic algorithms (GAs) have been proposed to provide remedies for drawbacks by avoiding local optima and improving the selection process itself. This manuscript presents a sweeping review on GA-based feature selection techniques in applications and their effectiveness across different domains.
  arXiv  Detail & Related papers  (2024-09-05T22:28:42Z)
• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• Modeling Local Search Metaheuristics Using Markov Decision Processes [0.0]
  We introduce a theoretical framework based on Markov Decision Processes (MDP) for analyzing local search metaheuristics. This framework not only helps in providing convergence results for individual algorithms, but also provides an explicit characterization of the exploration-exploitation tradeoff.
  arXiv  Detail & Related papers  (2024-07-29T11:28:30Z)
• A Taxonomy of Decentralized Identifier Methods for Practitioners [50.76687001060655]
  A core part of the new identity management paradigm of Self-Sovereign Identity (SSI) is the W3C Decentralized Identifiers (DIDs) standard. We propose a taxonomy of DID methods with the goal to empower practitioners to make informed decisions when selecting DID methods.
  arXiv  Detail & Related papers  (2023-10-18T13:01:40Z)
• A Survey of Contextual Optimization Methods for Decision Making under Uncertainty [47.73071218563257]
  This review article identifies three main frameworks for learning policies from data and discusses their strengths and limitations. We present the existing models and methods under a uniform notation and terminology and classify them according to the three main frameworks.
  arXiv  Detail & Related papers  (2023-06-17T15:21:02Z)
• Reinforcement Learning with Heterogeneous Data: Estimation and Inference [84.72174994749305]
  We introduce the K-Heterogeneous Markov Decision Process (K-Hetero MDP) to address sequential decision problems with population heterogeneity. We propose the Auto-Clustered Policy Evaluation (ACPE) for estimating the value of a given policy, and the Auto-Clustered Policy Iteration (ACPI) for estimating the optimal policy in a given policy class. We present simulations to support our theoretical findings, and we conduct an empirical study on the standard MIMIC-III dataset.
  arXiv  Detail & Related papers  (2022-01-31T20:58:47Z)
• Diversity in Kemeny Rank Aggregation: A Parameterized Approach [3.6603644500568806]
  A recent trend in artificial intelligence, called solution diversity, has focused on the development of notions of optimality. In this work, we investigate the impact of this combination in the field of Kemeny Rank Aggregation. Our main results work both when considering the traditional setting of aggregation over linearly ordered votes, and in the more general setting where votes are partially ordered.
  arXiv  Detail & Related papers  (2021-05-19T21:50:03Z)
• Exploiting Local Optimality in Metaheuristic Search [0.0]
  We introduce strategies to identify and take advantage of useful features of solution history with an adaptive memory metaheuristic. Our approach uses a new type of adaptive memory based on a construction called exponential extrapolation.
  arXiv  Detail & Related papers  (2020-10-12T01:51:09Z)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)
• On the Global Optimality of Model-Agnostic Meta-Learning [133.16370011229776]
  Model-a meta-learning (MAML) formulates meta-learning as a bilevel optimization problem, where the inner level solves each subtask based on a shared prior. We characterize optimality of the stationary points attained by MAML for both learning and supervised learning, where the inner-level outer-level problems are solved via first-order optimization methods.
  arXiv  Detail & Related papers  (2020-06-23T17:33:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.