The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and i...
Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implem...
Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that re...
In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results...
Combinatorial optimization problems include many NP-hard problems such as assignment, scheduling, and routing, for which conventional approaches to solving become computationally infeasible for large instances, thereby necessitating the use of metahe...
Open data makes it possible to gain insights into the transaction patterns of blockchain projects. These patterns can be modeled as transaction networks, which support a wide range of analytical techniques. Depending on the trade-off between informat...
Within the framework of the mathematical theory of conflicts, we consider a multi-criterial conflict situation using the example of a child–parent conflict. A general method for constructing a conflict diagram is described and possible ways of...
Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as...
This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm’s parameters during the run, to provide the metaheuristics with the ability to l...
When we face real problems using computational resources, we understand that it is common to find combinatorial problems in binary domains. Moreover, we have to take into account a large number of possible candidate solutions, since these can be nume...
Combinatorial optimization problems (COPs) are challenging for conventional computers because their solution spaces grow exponentially. To reduce exhaustive-search burden, hardware approaches have explored stochastic traversal of energy landscapes, i...
Combinatorial optimization problems (COPs) offer essential mathematical frameworks and algorithmic foundations for modeling complex real-world decision-making tasks. Recent advances in deep reinforcement learning (DRL) have shown promising results fo...
We consider one approach to formalize the Resource-Constrained Project Scheduling Problem (RCPSP) in terms of combinatorial optimization theory. The transformation of the original problem into combinatorial setting is based on interpreting each opera...
This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail...
This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions...
Industrial areas are set up on plots of roads and associated infrastructure. These use materials and machinery that have environmental impacts, and thus require constructive solutions throughout their lifecycles. In turn, these solutions and their co...
The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem...
In combinatorial optimization, the more complex a problem is, the more challenging it becomes, usually causing most research to focus on creating solvers for larger cases. However, real-life situations also contain small-sized instances that deserve...
Ant-inspired metaheuristic algorithms known as ant colony optimization (ACO) offer an approach that has the ability to solve complex problems in both discrete and continuous domains. ACOs have gained significant attention in the field of water resour...
Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristic...
Background: Combinatorial optimization problems (COPs) are central to Logistics and Supply Chain decision making, yet their NP-hardness prevents exact optimal solutions in reasonable time. Methods: This work addresses that limitation by developing a...
The capacitated vehicle routing problem (CVRP) is a representative NP-hard combinatorial optimization problem with broad applications in logistics and transportation. This paper proposes a hybrid metaheuristic, termed SPSOM-CVRP, which integrates an...
This work introduces a fuzzy optimization model, which solves in an integrated way the berth allocation problem (BAP) and the quay crane allocation problem (QCAP). The problem is solved for multiple quays, considering vessels’ imprecise arrival times...
Combinatorial Route Optimization (CRO) problems, such as the Vehicle Routing Problem (VRP) or the Travelling Salesman Problem (TSP), are commonly seen in scheduling, logistics, and transportation. While current machine learning (ML) methods can overc...
The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previou...
The Arctic Puffin Optimization (APO) Algorithm is a recently proposed metaheuristic algorithm that has been widely applied to solve optimization problems in continuous spaces. However, it cannot be directly used to solve combinatorial optimization pr...
The quadratic multidimensional knapsack problem (QMdKP) is a combinatorial optimization problem that involves selecting a subset of items to maximize both linear and quadratic profits without exceeding the capacity constraints across multiple dimensi...
The Knapsack Problem belongs to the best-studied classical problems in combinatorial optimization. The Multiple-choice Knapsack Problem (MCKP) represents a generalization of the problem, with various application fields such as industry, transportatio...
The Max-cut problem is a well-known combinatorial optimization problem, which has many real-world applications. However, the problem has been proven to be non-deterministic polynomial-hard (NP-hard), which means that exact solution algorithms are not...
Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning text, images, and audio to adeptly tackle complex problems. This study explores the ability of MLLMs in visually solving the Traveling Salesman Problem (TSP) and Multip...
Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs...
In this paper, we introduce a new combinatorial optimization problem entitled the color mix problem (CMP), which is a more general case of the grey pattern quadratic assignment problem (GP-QAP). Also, we propose an original hybrid genetic-iterated ta...
A plethora of combinatorial optimization problems can be linked to real-life decision scenarios. Even nowadays, more diverse and complex problems are popping up. One of these problems is the traveling thief problem (TTP), which combines elements from...
Recent studies have proposed post-processing strategies that iteratively reconstruct a partial segment of current solutions using Neural Combinatorial Optimization (NCO) solvers, thereby improving their performance on large-scale Vehicle Routing Prob...
The Set Coverage Problem (SCP) is an important combinatorial optimization problem known to be NP-complete. The use of metaheuristics to solve the SCP includes different algorithms. In particular, binarization techniques have been explored to adapt me...
Local search metaheuristics like tabu search or simulated annealing are popular heuristic optimization algorithms for finding near-optimal solutions for combinatorial optimization problems. However, it is still challenging for researchers and practit...
This paper presents a novel algorithm named Five-element Cycle Integrated Mutation Optimization (FECOIMO) for solving the Traveling Thief Problem (TTP). The algorithm introduces a five-element cycle structure that integrates various mutation operatio...
This paper addresses the Pickup and Delivery Problem with Time Windows (PDPTW), an NP-hard combinatorial optimization problem with major practical relevance in logistics and transportation. The study focuses on a quadratic unconstrained binary optimi...
The Traveling Salesman Problem (TSP) is a classical discrete combinatorial optimization problem that is widely applied in various domains, including robotics, transportation, networking, etc. Although existing studies have provided extensive discussi...
Large Language Models (LLMs) have immensely advanced the field of Artificial Intelligence (AI), with recent models being able to perform chain-of-thought reasoning and solve complex mathematical problems, ranging from theorem proving to ones involvin...
Metaheuristics have proven to be effective in solving large-scale combinatorial problems by combining global exploration with local exploitation, all within a reasonably short time. The balance between these phases is crucial to avoid slow or prematu...
The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem with broad applications in logistics and smart agriculture. However, despite significant progress in Transformer-based deep reinforcement learning methods, two major...
This research presents a novel hybrid approach, which combines the White Shark Optimizer (WSO) metaheuristic algorithm with chaotic maps integrated into the binarization process. Inspired by the predatory behavior of white sharks, WSO has shown great...
We address an inventory-routing problem that arises in a liquid oxygen-producing company. Decisions must be made for the efficient transport of the product from sources to destinations by means of a heterogeneous fleet of trucks. This combinatorial p...
In this article, we explore the use of meta-heuristic algorithms for costly black-box permutation optimization problems. These combinatorial problems are defined by solution spaces that consist of permutations of elements, with an objective function...
A structure-based lead optimization procedure is an essential step to finding appropriate ligand molecules binding to a target protein structure in order to identify drug candidates. This procedure takes a known structure of a protein-ligand complex...
The N-Queens problem is a fundamental challenge in combinatorial optimization, commonly used as a benchmark for assessing the efficiency of algorithms. Traditional algorithms, such as Backtracking with Forward Checking (BFC), constraint satisfaction...
Quadratic Unconstrained Binary Optimization (QUBO) is the problem of finding binary variable assignments that minimize a given quadratic objective function. Many combinatorial optimization problems can be transformed into equivalent QUBO formulations...
This paper introduces a novel model for virtual machine (VM) requests with predefined start and end times, referred to as scheduled virtual machine demands (SVMs). In cloud computing environments, SVMs represent anticipated resource requirements deri...
Diffusion models have shown strong promise for neural combinatorial optimization, yet existing approaches define the forward noising process over a fully connected graph, discarding the sparse topological structure inherent to the Traveling Salesman...
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