Jin-Kao Hao

University of Angers | UA · Département d'informatique

Continuous p-dispersion problems with and without boundary constraints are NP-hard optimization problems with numerous real-world applications, notably in facility location and circle packing, which are widely studied in mathematics and operations research. In this work, we concentrate on general cases with a nonconvex multiply connected region tha...

The software and data in this repository are a snapshot of the software and data that were used in the research reported in the paper An efficient optimization model and tabu search-based global optimization approach for continuous p-dispersion problem by L.J. Lai, Z.H. Lin, J.K. Hao, and Q.H. Wu.

The family traveling salesman problem with incompatibility constraints (FTSP-IC) is a variant of the well-known traveling salesman problem. Given a set of candidate nodes divided into several subsets (families), the FTSP-IC is to find several routes such that the sum of their total traveling distance is minimized, while ensuring a predetermined num...

The knapsack problem (KP) with forfeits is a generalized KP that aims to select some items, among a set of candidate items, to maximize a profit function without exceeding the knapsack capacity. Moreover, a forfeit cost is incurred and deducted from the profit function when both incompatible items are placed in the knapsack. This problem is a relev...

Hospital admission management is a fundamental challenge due to the uncertain demand for inpatient beds. This paper studies a stochastic variant of the well-known patient admission scheduling problem, which aims to assign patients to rooms during their hospitalizations while considering the overstay risk. We consider the problem as a two-stage stoc...

Continuous p-dispersion problems with and without boundary constraints are NP-hard optimization problems with numerous real-world applications, notably in facility location and circle packing, which are widely studied in mathematics and operations research. In this work, we concentrate on general cases with a non-convex multiply-connected region th...

Rank aggregation combines the preference rankings of multiple alternatives from different voters into a single consensus ranking, providing a useful model for a variety of practical applications but posing a computationally challenging problem. In this paper, we provide an effective hybrid evolutionary ranking algorithm to solve the rank aggregatio...

This study considers a well-known critical node detection problem that aims to minimize a pairwise connectivity measure of an undirected graph via the removal of a subset of nodes (referred to as critical nodes) subject to a cardinality constraint. Potential applications include epidemic control, emergency response, vulnerability assessment, carbon...

The cumulative capacitated vehicle routing problem with single (CCVRP) or multiple depots (MDCCVRP) is a variant of the popular capacitated vehicle routing problem. Instead of minimizing the total travel time, the objective here is to minimize the sum of all customers’ waiting times. This problem has a variety of real-world applications, especially...

We present a memetic algorithm with adaptive operator selection for k-coloring and weighted vertex coloring. Our method uses online selection to adaptively determine the couple of crossover and local search operators to apply during the search to improve the efficiency of the algorithm. This leads to better results than without the operator selecti...

In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains. This method relies on the maximization of a penalized likelihood score. It exploits all available information about both the sequence of the different symbols and their arrival times. A theoretical analysis is carried out to prove that minimizing t...

Rank aggregation combines the preference rankings of multiple alternatives from different voters into a single consensus ranking, providing a useful model for a variety of practical applications, but posing a computationally challenging problem. In this paper, we provide an effective hybrid evolutionary ranking algorithm to solve the rank aggregati...

The Hamiltonian p ‐median problem consists of finding p ( is given) non‐intersecting Hamiltonian cycles in a complete edge‐weighted graph such that each cycle visits at least three vertices and each vertex belongs to exactly one cycle, while minimizing the total cost of p cycles. In this work, we present an effective and scalable hybrid genetic alg...

This chapter is dedicated to memetic algorithms for diversity and dispersion problems. It is organized in two parts. The first part discusses the general design issues of memetic algorithms concerning crossover, local search, population management, evaluation function, and constraint handling. The second part presents a survey of memetic algorithms...

We propose an approach to improve the classification performance of the Tangled Programs Graph (TPG). TPG is a genetic programming method that aims to discover Directed Acyclic Graphs (DAGs) through an evolutionary process, where the edges carry programs that allow nodes to create a route from the root to a leaf, and the leaves represent actions or...

Given a graph, a k-plex is a set of vertices in which each vertex is not adjacent to at most k−1 other vertices in the set. The maximum k-plex problem, which asks for the largest k-plex from the given graph, is an important but computationally challenging problem in applications such as graph mining and community detection. So far, there are many p...

Product development projects usually contain many interrelated activities with complex information dependences, which induce activity rework, project delay and cost overrun. To reduce negative impacts, scheduling interrelated activities in an appropriate sequence is an important issue for project managers. This study develops a double-decomposition...

Product development projects usually contain many interrelated activities with complex information dependences, which induce activity rework, project delay and cost overrun. To reduce negative impacts, scheduling interrelated activities in an appropriate sequence is an important issue for project managers. This study develops a double-decomposition...

This study considers a well-known critical node detection problem that aims to minimize a pairwise connectivity measure of an undirected graph via the removal of a sub-set of nodes (referred to as critical nodes) subject to a cardinality constraint. Potential appli-cations include epidemic control, emergency response, vulnerability assessment, carb...

The distance-based critical node problem involves identifying a subset of nodes in a graph such that the removal of these nodes leads to a residual graph with the minimum distance-based connectivity. Due to its NP-hard nature, solving this problem using exact algorithms has proved to be highly challenging. Moreover, existing heuristic algorithms ar...

The orienteering problem (OP) and prize-collecting traveling salesman problem(PCTSP) are two typical TSPs with profits, in which each vertex has a profit and the goal is to visit several vertices to optimize the collected profit and travel costs. The OP aims to collect the maximum profit without exceeding the given travel cost. The PCTSP seeks to m...

This work investigates the Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. In addition to the basic MCTS algorithm, we study several MCTS variants where the conventional random simulation is replaced by other simulation strategies including greedy and local search heuristics...

This work presents a hyper-heuristic approach to online learning, which combines Monte Carlo Tree Search with multiple local search operators selected on the fly during the search. The impacts of different operator policies, including proportional bias, one-armed bandit, and neural network, are investigated. Experiments on well-known benchmarks of...

This paper deals with the problem of deinterleaving a sequence of signals received from different emitters at different time steps. It is assumed that this pulse sequence can be modeled by a collection of processes over disjoint finite sub-alphabets, which have been randomly interleaved by a switch process. A known method to solve this problem is t...

This paper presents an effective perturbation-based thresholding search for two popular and challenging packing problems with minimal containers: packing N identical circles in a square and packing N identical spheres in a cube. Following the penalty function approach, we handle these constrained optimization problems by solving a series of unconst...

Given a directed graph G = ( V, E ), a feedback vertex set is a vertex subset C whose removal makes the graph G acyclic. The feedback vertex set problem is to find the subset C * whose cardinality is the minimum. As a general model, this problem has a variety of applications. However, the problem is known to be NP-hard, and thus computationally cha...

The boolean quadratic programming problem with generalized upper bound constraints (BQP-GUB) is an NP-hard problem with many practical applications. In this study, we propose an effective multi-wave tabu search algorithm for solving BQP-GUB. The algorithm performs a sequence of search waves, where each wave alternates between the forward and revers...

The minmax multiple traveling salesman problem with single depot (the minmax mTSP) or multiple depots (the minmax multidepot mTSP) aims to minimize the longest tour among a set of tours. These two minmax problems are useful for a variety of real-life applications and typically studied separately in the literature. We propose a unified memetic appro...

The split delivery vehicle routing problem is a variant of the well-known vehicle routing problem, where each customer can be visited by several vehicles. The problem has many practical applications, but it is computationally challenging. This paper presents an effective memetic algorithm for solving the problem with a fleet of limited or unlimited...

Given a set of facilities, a set of positions arranged in a loop configuration and a flow cost matrix, the bidirectional loop layout problem (BLLP) is to find a facility-to-position assignment with the minimum sum of the products of flow costs and facility distances. The flow cost between two facilities is the unit cost of transporting items betwee...

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. In this paper we present a new framework that combines a deep neural network with the best tools of classical heuristics for graph coloring. The proposed method is ev...

We present an iterated hyperplane search approach for the budgeted maximum coverage problem. Our algorithm relies on the idea of searching on specific areas identified by cardinality-constrained hyperplanes. It combines three complementary procedures: a tabu search procedure to identify a promising hyperplane, a hyperplane search procedure to exami...

The clique partitioning problem (CPP) of an edge-weighted complete graph is to partition the vertex set V into k disjoint subsets such that the sum of the edge weights within all cliques induced by the subsets is as large as possible. The problem has a number of practical applications in areas, such as data mining, engineering, and bioinformatics,...

The Clustered Traveling Salesman Problem (CTSP) is a variant of the popular Traveling Salesman Problem (TSP) arising from a number of real-life applications. In this work, we explore a transformation approach that solves the CTSP by converting it to the well-studied TSP. For this purpose, we first investigate a technique to convert a CTSP instance...

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. Starting with the basic MCTS algorithm, we gradually introduce a number of algorithmic variants where MCTS is extended by various simulation strategies including greedy an...

The multiple traveling repairman problem with profits consists of multiple repairmen serving a subset of all customers to maximize the revenues collected through the visited customers. To address this problem, an effective hybrid search algorithm based on the memetic framework is proposed. In the proposed method, three features are integrated: a de...

The Traveling Repairman Problem with Profits is to select a subset of nodes (customers) in a weighted graph to maximize the collected time-dependent revenues. We introduce an intensification-driven local search algorithm for solving this challenging problem. The key feature of the algorithm is an intensification mechanism that intensively investiga...

We present frequent pattern-based search (FPBS) that combines data mining and optimization. FPBS is a general-purpose method that unifies data mining and optimization within the population-based search framework. The method emphasizes the relevance of a modular- and component-based approach, making it applicable to optimization problems by instanti...

The Steiner tree problem, which asks for a minimum weighted tree spanning a given set of terminal vertices in a given graph, is a classic problem arising in numerous practical applications. Many algorithms about this problem emerged in the past decade, especially presented in the 11th DIMACS Challenge in 2014 and the 3rd PACE Competition in 2018. I...

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. Starting with the basic MCTS algorithm, we gradually introduce a number of algorithmic variants where MCTS is extended by various simulation strategies including greedy an...

Effective decision support systems are very useful management tools in many applied domains. However, such systems are still scarce or even missing in social and medico-social establishments. This study investigates the personalized user project planning problem in French social and medico-social establishments, whose purpose is to optimize the ass...

We present an effective hybrid algorithm with neighborhood reduction for solving the multiple traveling salesman problem (mTSP). This problem aims to optimize one of the two objectives: to minimize the total traveling distance (the minsum mTSP) or to minimize the longest tour (the minmax mTSP). The proposed algorithm hybridizes inter-tour optimizat...

Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking. As a useful model for a variety of practical applications, however, it is a computationally challenging problem. In this paper, we propose an effective hybrid evolutionary ranking algorithm to solve the rank ag...

The single row facility layout problem (SRFLP) is concerned with arranging facilities along a straight line so as to minimize the sum of the products of the flow costs and distances among all facility pairs. SRFLP has rich practical applications and is however NP-hard. In this paper, we first investigate a dedicated symmetry-breaking approach based...

Metaheuristic algorithms are practically used to produce approximate solutions to large QUBO instances that cannot be solved exactly due to the high computational complexity. This chapter is dedicated to a review on the general metaheuristic approach for solving the QUBO. First, we present some basic components of local search that are widely used...

The gate assignment problem (GAP) is an important task in airport management. This study investigates an original probability learning based heuristic algorithm for solving the problem. The proposed algorithm relies on a mixed search strategy exploring both feasible and infeasible solutions with the tabu search method and employs a reinforcement le...