Search Results for author: Jin-Kao Hao

Found 19 papers, 4 papers with code

On Monte Carlo Tree Search for Weighted Vertex Coloring

1 code implementation3 Feb 2022 Cyril Grelier, Olivier Goudet, Jin-Kao Hao

This work presents the first study of using the popular Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem.

Heuristic Search for Rank Aggregation with Application to Label Ranking

no code implementations11 Jan 2022 Yangming Zhou, Jin-Kao Hao, Zhen Li, Fred Glover

Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking.

An effective hybrid search algorithm for the multiple traveling repairman problem with profits

1 code implementation9 Nov 2021 Jintong Ren, Jin-Kao Hao, Feng Wu, Zhang-Hua Fu

As an extension of the traveling repairman problem with profits, the multiple traveling repairman problem with profits consists of multiple repairmen who visit a subset of all customers to maximize the revenues collected through the visited customers.

A deep learning guided memetic framework for graph coloring problems

1 code implementation13 Sep 2021 Olivier Goudet, Cyril Grelier, Jin-Kao Hao

Given an undirected graph $G=(V, E)$ with a set of vertices $V$ and a set of edges $E$, a graph coloring problem involves finding a partition of the vertices into different independent sets.

A massively parallel evolutionary algorithm for the partial Latin square extension problem

no code implementations18 Mar 2021 Olivier Goudet, Jin-Kao Hao

The partial Latin square extension problem is to fill as many as possible empty cells of a partially filled Latin square.

A threshold search based memetic algorithm for the disjunctively constrained knapsack problem

no code implementations12 Jan 2021 Zequn Wei, Jin-Kao Hao

The disjunctively constrained knapsack problem consists in packing a subset of pairwisely compatible items in a capacity-constrained knapsack such that the total profit of the selected items is maximized while satisfying the knapsack capacity.

Probability Learning based Tabu Search for the Budgeted Maximum Coverage Problem

no code implementations12 Jul 2020 Liwen Li, Zequn Wei, Jin-Kao Hao, Kun He

As the counterpart problem of SUKP, however, BMCP was introduced early in 1999 but since then it has been rarely studied, especially there is no practical algorithm proposed.

Solving the Clustered Traveling Salesman Problem via TSP methods

no code implementations10 Jul 2020 Yongliang Lu, Jin-Kao Hao, Qinghua Wu

The Clustered Traveling Salesman Problem (CTSP) is a variant of the popular Traveling Salesman Problem (TSP) arising from a number of real-life applications.

Traveling Salesman Problem

Variable Population Memetic Search: A Case Study on the Critical Node Problem

no code implementations12 Sep 2019 Yangming Zhou, Jin-Kao Hao, Zhang-Hua Fu, Zhe Wang, Xiangjing Lai

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems.

Population-based Gradient Descent Weight Learning for Graph Coloring Problems

1 code implementation5 Sep 2019 Olivier Goudet, Béatrice Duval, Jin-Kao Hao

Unlike existing methods for graph coloring that are specific to the considered problem, the presented work targets a generic objective by introducing a unified method that can be applied to different graph coloring problems.

Iterated two-phase local search for the Set-Union Knapsack Problem

no code implementations12 Mar 2019 Zequn Wei, Jin-Kao Hao

The Set-union Knapsack Problem (SUKP) is a generalization of the popular 0-1 knapsack problem.

Combinatorial Optimization

Combining tabu search and graph reduction to solve the maximum balanced biclique problem

no code implementations20 May 2017 Yi Zhou, Jin-Kao Hao

The Maximum Balanced Biclique Problem is a well-known graph model with relevant applications in diverse domains.

Memetic search for identifying critical nodes in sparse graphs

no code implementations11 May 2017 Yangming Zhou, Jin-Kao Hao, Fred Glover

In this paper, we study the classic critical node problem (CNP) and introduce an effective memetic algorithm for solving CNP.

Diversification-Based Learning in Computing and Optimization

no code implementations23 Mar 2017 Fred Glover, Jin-Kao Hao

Diversification-Based Learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization.

Metaheuristic Optimization

Reinforcement learning based local search for grouping problems: A case study on graph coloring

no code implementations1 Apr 2016 Yangming Zhou, Jin-Kao Hao, Béatrice Duval

Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints.

Combinatorial Optimization reinforcement-learning

On memetic search for the max-mean dispersion problem

no code implementations3 Mar 2015 Xiangjing Lai, Jin-Kao Hao

Computational experiments on the set of 160 benchmark instances with up to 1000 elements commonly used in the literature show that the proposed algorithm improves or matches the published best known results for all instances in a short computing time, with only one exception, while achieving a high success rate of 100\%.

A Memetic Algorithm for the Linear Ordering Problem with Cumulative Costs

no code implementations18 May 2014 Tao Ye, Kan Zhou, Zhipeng Lu, Jin-Kao Hao

This paper introduces an effective memetic algorithm for the linear ordering problem with cumulative costs.

A Multi-parent Memetic Algorithm for the Linear Ordering Problem

no code implementations18 May 2014 Tao Ye, Tao Wang, Zhipeng Lu, Jin-Kao Hao

In this paper, we present a multi-parent memetic algorithm (denoted by MPM) for solving the classic Linear Ordering Problem (LOP).

A Three-Phase Search Approach for the Quadratic Minimum Spanning Tree Problem

no code implementations6 Feb 2014 Zhang-Hua Fu, Jin-Kao Hao

Given an undirected graph with costs associated with each edge as well as each pair of edges, the quadratic minimum spanning tree problem (QMSTP) consists of determining a spanning tree of minimum total cost.

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