Mohamed El Yafrani

Aalborg University · Department of Materials and Production

Taking fast action, and effectively utilizing the available resources, are important when conducting time-critical surveillance missions. In addition, the potential complexity of the search, such as the ruggedness of the terrain or large size of the search region, should be considered. Such issues can be tackled by using a heterogeneous fleet of ma...

Problems with multiple interdependent components offer a better representation of the real-world situations where globally optimal solutions are preferred over optimal solutions for the individual components. One such model is the Travelling Thief Problem (TTP); while it may offer a better benchmarking alternative to the standard models, only one f...

In this study, we tackle a key scheduling problem in a robotic arm-based food processing system, where multiple conveyors—an infeed conveyor that feeds food items to robotic arms and two tray lane conveyors, on which trays to batch food items are placed—are implemented. The target scheduling problem is to determine what item on an infeed conveyor b...

Problems with multiple interdependent components offer a better representation of the real-world situations where globally optimal solutions are preferred over optimal solutions for the individual components. One such model is the Travelling Thief Problem (TTP); while is appears popular and while it may offer a better benchmarking alternative to th...

Since its inception in 2013, the Travelling Thief Problem (TTP) has been widely studied as an example of problems with multiple interconnected sub-problems. The dependency in this model arises when tying the travelling time of the "thief" to the weight of the knapsack. However, other forms of dependency as well as combinations of dependencies shoul...

Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such a...

During the evolutionary process, algorithms based on probability distributions for generating new individuals suffer from computational burden due to the intensive computation of probability distribution estimations, particularly when using Probabilistic Graph Models (PGMs). In the Bayesian Optimisation Algorithm (BOA), for instance, determining th...

This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), applied to the MNK Landscape combinatorial problem. In the experiments, we evaluate the performance considering three different aspects: optimizati...

In practice, allocating tasks to resources is often tackled in (near) real-time due to the latency of the task information and sudden task arrivals into a system. Therefore, the problem must be solved within a very short time budget, when tasks are urgent or idle resources are critical to the system's performance. Local search algorithms could be a...

In this paper, we introduce a Model-based Algorithm Tuning Engine, namely MATE, where the parameters of an algorithm are represented as expressions of the features of a target optimisation problem. In contrast to most static (feature-independent) algorithm tuning engines such as irace and SPOT, our approach aims to derive the best parameter configu...

A Local Optima Network (LON) is a graph model that compresses the fitness landscape of a particular combinatorial optimization problem based on a specific neighborhood operator and a local search algorithm. Determining which and how landscape features affect the effectiveness of search algorithms is relevant for both predicting their performance an...

In this paper, we introduce a Model-based Algorithm Turning Engine, namely MATE, where the parameters of an algorithm are represented as expressions of the features of a target optimisation problem. In contrast to most static (feature-independent) algorithm tuning engines such as irace and SPOT, our approach aims to derive the best parameter config...

Recognising that real-world optimisation problems have multiple interdependent components can be quite easy. However, providing a generic and formal model for dependencies between components can be a tricky task. In fact, a PMIC can be considered simply as a single optimisation problem and the dependencies between components could be investigated b...

The Bayesian Optimisation Algorithm (BOA) is an Estimation of Distribution Algorithm (EDA) that uses a Bayesian network as probabilistic graphical model (PGM). Determining the optimal Bayesian network structure given a solution sample is an NP-hard problem. This step should be completed at each iteration of BOA, resulting in a very time-consuming p...

Local Optima Networks are models proposed to understand the structure and properties of combinatorial landscapes. The fitness landscape is explored as a graph whose nodes represent the local optima (or basins of attraction) and edges represent the connectivity between them. In this paper, we use this representation to study a combinatorial optimisa...

Since its inauguration in 1966, the ACM A. M. Turing Award has recognized major contributions of lasting importance in computing. Through the years, it has become the most prestigious technical award in the field, often referred to as the "Nobel Prize of computing." During 2017, ACM celebrated 50 years of the Turing Award and the visionaries who ha...

Fitness landscape analysis investigates features with a high influence on the performance of optimization algorithms, aiming to take advantage of the addressed problem characteristics. In this work, a fitness landscape analysis using problem features is performed for a Multi-objective Bayesian Optimization Algorithm (mBOA) on instances of MNK-lands...

In this paper, we investigate the use of hyper-heuristics for the travelling thief problem (TTP). TTP is a multi-component problem, which means it has a composite structure. The problem is a combination between the travelling salesman problem and the knapsack problem. Many heuristics were proposed to deal with the two components of the problem sepa...

The sequencing of DNA goes through a step of fragment assembly, this step is known as DNA fragment assembly problem (FAP). Fragment assembly is considered as an NP-hard problem, which means there is no known polynomial-time exact approach, hence the need for meta-heuristics. Three major strategies are widely used to tackle this problem: greedy grap...

Many real-world problems are composed of multiple interacting sub-problems. However, few investigations have been carried out to look into tackling problems from a metaheuristics perspective. The Traveling Thief Problem (TTP) is a new NP-hard problem with two interdependent components that aim to provide a benchmark model to better represent this c...

Hyper-heuristics are high-level search techniques which improve the performance of heuristics operating at a higher heuristic level. Usually, these techniques automatically generate or select new simpler components based on the feedback received during the search. Estimation of Distribution Algorithms (EDAs) have been applied as hyper-heuristics, u...

Multi-component problems are optimization problems that are composed of multiple interacting sub problems. The motivation of this work is to investigate whether it can be better to consider multiple objectives when dealing with multiple interdependent components. Therefore, the Travelling Thief Problem (TTP), a relatively new benchmark problem, is...

The Travelling Thief Problem (TTP) is a novel problem that aims to provide a benchmark model of combinatorial optimization problems with multiple interdependent components. The TTP combines two other well known benchmark problems: the Travelling Salesman Problem (TSP) and the Knapsack Problem (KP). The aim of this paper is to study the interdepende...

The Travelling Thief Problem (TTP) is an optimization problem introduced in order to provide a more realistic model for real-world optimization problems. The problem combines the Travelling Salesman Problem and the Knapsack Problem and introduces the notion of interdependence between sub-problems. In this paper, we study and compare different appro...

Real-world problems are very difficult to optimize. However, many researchers have been solving benchmark problems that have been extensively investigated for the last decades even if they have very few direct applications. The Traveling Thief Problem (TTP) is a NP-hard optimization problem that aims to provide a more realistic model. TTP targets p...

This extended abstract presents an overview on NP-hard optimization problems with multiple interdependent components. These problems occur in many real-world applications: industrial applications, engineering, and logistics. The fact that these problems are composed of many sub-problems that are NP-hard makes them even more challenging to solve usi...

Real-world problems are very difficult to optimize. However, many researchers have been solving benchmark problems that have been extensively investigated for the last decades even if they have very few direct applications. The Traveling Thief Problem (TTP) is a NP-hard optimization problem that aims to provide a more realistic model. TTP targets p...