Evolutionary Algorithm with Detection Region Method for Constrained Multi-Objective Problems with Binary Constraints

• URL: http://arxiv.org/abs/2411.08437v1
• Date: Wed, 13 Nov 2024 08:39:04 GMT
• Title: Evolutionary Algorithm with Detection Region Method for Constrained Multi-Objective Problems with Binary Constraints
• Authors: Weixiong Huang, Rui Wang, Tao Zhang, Sheng Qi, Ling Wang,
• Abstract summary: This paper proposes a novel algorithm called DRMCMO based on the detection region method. In DRMCMO, detection regions dynamic monitor feasible solutions to enhance convergence, helping the population escape local optima. We have modified three existing test suites to serve as benchmark test problems for CMOPs with binary constraints.
• Score: 9.764702512419946
• License:
• Abstract: Solving constrained multi-objective optimization problems (CMOPs) is a challenging task. While many practical algorithms have been developed to tackle CMOPs, real-world scenarios often present cases where the constraint functions are unknown or unquantifiable, resulting in only binary outcomes (feasible or infeasible). This limitation reduces the effectiveness of constraint violation guidance, which can negatively impact the performance of existing algorithms that rely on this approach. Such challenges are particularly detrimental for algorithms employing the epsilon-based method, as they hinder effective relaxation of the feasible region. To address these challenges, this paper proposes a novel algorithm called DRMCMO based on the detection region method. In DRMCMO, detection regions dynamic monitor feasible solutions to enhance convergence, helping the population escape local optima. Additionally, these regions collaborate with the neighbor pairing strategy to improve population diversity within narrow feasible areas. We have modified three existing test suites to serve as benchmark test problems for CMOPs with binary constraints(CMOP/BC) and conducted comprehensive comparative experiments with state-of-the-art algorithms on these test suites and real-world problems. The results demonstrate the strong competitiveness of DRMCMO against state-of-the-art algorithms. Given the limited research on CMOP/BC, our study offers a new perspective for advancing this field.

Related papers

• Deep Reinforcement Learning for Online Optimal Execution Strategies [49.1574468325115]
  This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) We show that our algorithm successfully approximates the optimal execution strategy.
  arXiv  Detail & Related papers  (2024-10-17T12:38:08Z)
• Provably Efficient Exploration in Inverse Constrained Reinforcement Learning [12.178081346315523]
  Inverse Constrained Reinforcement Learning seeks to recover constraints from expert demonstrations in a data-driven manner. We introduce a strategic exploration framework with guaranteed efficiency. Motivated by our findings, we propose two exploratory algorithms to achieve efficient constraint inference.
  arXiv  Detail & Related papers  (2024-09-24T10:48:13Z)
• A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints [66.61399765513383]
  We develop a BLOCC algorithm to tackle BiLevel Optimization problems with Coupled Constraints. We demonstrate its effectiveness on two well-known real-world applications.
  arXiv  Detail & Related papers  (2024-06-14T15:59:36Z)
• Modified LAB Algorithm with Clustering-based Search Space Reduction Method for solving Engineering Design Problems [0.7789406630452325]
  A modified LAB algorithm is introduced in this paper. The proposed algorithm incorporates the roulette wheel approach and a reduction factor introducing inter-group competition. The algorithm exhibited improved and superior robustness as well as search space exploration capabilities.
  arXiv  Detail & Related papers  (2023-10-04T12:35:13Z)
• New Characterizations and Efficient Local Search for General Integer Linear Programming [17.80124240223163]
  This work proposes new characterizations of linear programming (ILP) with the concept of boundary solutions. We develop a new local search algorithm Local-ILP, which is efficient for solving general ILP validated on a large heterogeneous problem dataset. Experiments conducted on the MIPLIB dataset show the effectiveness of our algorithm in solving large-scale hard ILP problems.
  arXiv  Detail & Related papers  (2023-04-29T07:22:07Z)
• 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)
• Instance-Dependent Confidence and Early Stopping for Reinforcement Learning [99.57168572237421]
  Various algorithms for reinforcement learning (RL) exhibit dramatic variation in their convergence rates as a function of problem structure. This research provides guarantees that explain textitex post the performance differences observed. A natural next step is to convert these theoretical guarantees into guidelines that are useful in practice.
  arXiv  Detail & Related papers  (2022-01-21T04:25:35Z)
• 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)
• Improving Solution Quality of Bounded Max-Sum Algorithm to Solve DCOPs involving Hard and Soft Constraints [3.0969191504482243]
  Bounded Max-Sum (BMS) is a message-passing algorithm that provides approximation solution to a specific form of de-centralized coordination problems. In particular, BMS algorithm is able to solve problems of this type having large search space at the expense of low computational cost.
  arXiv  Detail & Related papers  (2020-12-02T18:10:14Z)
• The limits of min-max optimization algorithms: convergence to spurious non-critical sets [82.74514886461257]
  min-max optimization algorithms encounter problems far greater because of the existence of periodic cycles and similar phenomena. We show that there exist algorithms that do not attract any points of the problem. We illustrate such challenges in simple to almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost
  arXiv  Detail & Related papers  (2020-06-16T10:49:27Z)

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.