Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems

• URL: http://arxiv.org/abs/2407.08754v1
• Date: Sat, 29 Jun 2024 15:19:46 GMT
• Title: Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems
• Authors: Noor A. Rashed, Yossra H. Ali, Tarik A. Rashid, A. Salih,
• Abstract summary: Multi-Objective Optimization (MOO) techniques have become increasingly popular in recent years. This paper examines recently developed MOO-based algorithms. In real-world case studies, MOO algorithms address complicated decision-making challenges.
• Score: 4.023511716339818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-Objective Optimization (MOO) techniques have become increasingly popular in recent years due to their potential for solving real-world problems in various fields, such as logistics, finance, environmental management, and engineering. These techniques offer comprehensive solutions that traditional single-objective approaches fail to provide. Due to the many innovative algorithms, it has been challenging for researchers to choose the optimal algorithms for solving their problems. This paper examines recently developed MOO-based algorithms. MOO is introduced along with Pareto optimality and trade-off analysis. In real-world case studies, MOO algorithms address complicated decision-making challenges. This paper examines algorithmic methods, applications, trends, and issues in multi-objective optimization research. This exhaustive review explains MOO algorithms, their methods, and their applications to real-world problems. This paper aims to contribute further advancements in MOO research. No singular strategy is superior; instead, selecting a particular method depends on the natural optimization problem, the computational resources available, and the specific objectives of the optimization tasks.

Related papers

• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization [11.935678130032766]
  We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by MOO algorithms are scattered across fields.
  arXiv  Detail & Related papers  (2023-11-19T10:24:39Z)
• 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)
• A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization [2.9882027965916413]
  Influence Maximization (IM) is a classical optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. Main challenge comes from the NP-hardness of the IM problem and #P-hardness of estimating the influence spread. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research.
  arXiv  Detail & Related papers  (2022-11-06T10:13:42Z)
• Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics [89.24951036534168]
  Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model.
  arXiv  Detail & Related papers  (2022-10-23T22:21:10Z)
• Do Current Multi-Task Optimization Methods in Deep Learning Even Help? [35.27168056803643]
  We show that, despite the added design and computational complexity of these algorithms, MTO methods do not yield any performance improvements beyond what is achievable via traditional optimization approaches. We highlight alternative strategies that consistently yield improvements to the performance profile and point out common training pitfalls that might cause suboptimal results.
  arXiv  Detail & Related papers  (2022-09-23T02:45:13Z)
• Multi-Objective Policy Gradients with Topological Constraints [108.10241442630289]
  We present a new algorithm for a policy gradient in TMDPs by a simple extension of the proximal policy optimization (PPO) algorithm. We demonstrate this on a real-world multiple-objective navigation problem with an arbitrary ordering of objectives both in simulation and on a real robot.
  arXiv  Detail & Related papers  (2022-09-15T07:22:58Z)
• A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [62.997667081978825]
  This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms. We distinguish between metaheuristic-based algorithms, metamodel-based algorithms, and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.
  arXiv  Detail & Related papers  (2021-11-23T10:22:30Z)
• An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem [49.04543375851723]
  Vehicle routing problem (VRP) is a typical discrete optimization problem. Many studies consider learning-based optimization algorithms to solve VRP. This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.
  arXiv  Detail & Related papers  (2021-07-15T02:13:03Z)
• PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems [0.0]
  The proposed algorithm is based on solving a set of surrogate problems defined by models of the real one. Our algorithm also performs a meta-search for optimal surrogate models and navigation strategies for the optimization landscape.
  arXiv  Detail & Related papers  (2021-03-19T11:18:03Z)
• Bio-inspired Optimization: metaheuristic algorithms for optimization [0.0]
  In today's day and time solving real-world complex problems has become fundamentally vital and critical task. Traditional optimization methods are found to be effective for small scale problems. For real-world large scale problems, traditional methods either do not scale up or fail to obtain optimal solutions or they end-up giving solutions after a long running time.
  arXiv  Detail & Related papers  (2020-02-24T13:26:34Z)

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.