Mohamed El Yafrani

Population-based vs. Single-solution Heuristics for the Travelling Thief Problem

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 approaches for solving the TTP from a metaheuristics perspective. Two heuristic algorithms are proposed. The first is a Memetic Algorithm, and the second is a single-solution heuristic empowered by Hill Climbing and Simulated Annealing. Two other state-of-the-art algorithms are briefly revisited, analyzed, and compared to our algorithms. The obtained results prove that our algorithms are very efficient for many TTP instances.

A local search based approach for solving the Travelling Thief Problem

Graphical abstractDisplay Omitted HighlightsInvestigate the interdependence in real-world optimization problems.Propose a local search based approach to solve efficiently TTP instances.The proposed algorithms are designed to exploit particular search areas.A starting point for investigating neighborhood based algorithms for the TTP. 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 interdependence between the TTPs components, and how it makes solving each sub-problem independently from the other useless for solving the overall problem. A local search approach is proposed to solve the TTP. Two simple iterative neighborhood algorithms based on our approach are presented, analyzed, and compared to other algorithms. Initialization strategies are empirically investigated. The experimental results confirm that our approach was able to find new better solutions for many TTP instances.

HSEDA: a heuristic selection approach based on estimation of distribution algorithm for the travelling thief problem

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, using a probabilistic distribution model to extract and represent interactions between heuristics and its low-level components to provide high-valued problem solutions. In this paper, we consider an EDA-based hyper-heuristic framework which encompasses a Heuristic Selection approach aiming to find best combinations of different known heuristics. A surrogate assisted model evaluates the new heuristic combinations sampled by the EDA probabilistic model using an approximation function. We compare our proposed approach named Heuristic Selection based on Estimation of Distribution Algorithm (HSEDA) with three state-of-the-art algorithms for the Travelling Thief Problem (TTP). The experimental results show that the approach is competitive, outperforming the other algorithms on most of the medium-sized TTP instances considered in this paper.

Cosolver2B: An efficient local search heuristic for the Travelling Thief Problem

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 particularly routing problem under packing/loading constraints which can be found in supply chain management and transportation. In this paper, TTP is presented and formulated mathematically. A combined local search algorithm is proposed and compared with Random Local Search (RLS) and Evolutionary Algorithm (EA). The obtained results are quite promising since new better solutions were found.

Efficiently solving the Traveling Thief Problem using hill climbing and simulated annealing

Abstract 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 category of problems. In this paper, TTP is investigated theoretically and empirically. Two algorithms based on a 2-OPT steepest ascent hill climbing algorithm and the simulated annealing metaheuristic named CS2SA* and CS2SA-R are proposed to solve the problem. The obtained results show that the proposed algorithms are efficient for many TTP instances of different sizes and properties and are very competitive in comparison with two of the best-known state-of-the-art algorithms.

A hyperheuristic approach based on low-level heuristics for the travelling thief problem

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 separately. In this work, we investigate the use of automatic online heuristic selection in order to find the best combination of the different known heuristics. In order to achieve this, we propose a genetic programming based hyper-heuristic called GPHS*, and compare it to state-of-the-art algorithms. The experimental results show that the approach is competitive with those algorithms on small and mid-sized TTP instances.

Multi-objectiveness in the single-objective traveling thief problem

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 investigated as a bi-objective problem. The results indicate that a multi-objective approach can produce solutions to the single-objective TTP variant while being competitive to current state-of-the-art solvers.

A fitness landscape analysis of the travelling thief problem

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 optimisation problem, with two interdepend components, named the Travelling Thief Problem (TTP). The objective is to understand the search space structure of the TTP using basic local search heuristics and to distinguish the most impactful problem features. We create a large set of enumerable TTP instances and generate a Local Optima Network for each instance using two hill climbing variants. Two problem features are investigated, namely the knapsack capacity and profit-weight correlation. Our insights can be useful not only to design landscape-aware local search heuristics, but also to better understand what makes the TTP challenging for specific heuristics.

A hybrid crow search algorithm for solving the DNA fragment assembly problem

Abstract 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 graph-based algorithms, de Bruijn graphs, and the overlay-layout-consensus (OLC) approach. In this paper, we propose an adaptation of the novel crow search algorithm (CSA) to solve the DNA fragment assembly problem following the OLC model. In order to accelerate the search process and improve the quality of the solutions, we combined CSA with a local search method. Using this combination we were able to obtain very accurate solutions for all the instances of the DNA fragment assembly problem we tested. In fact, our algorithm outperformed all other algorithms designed for the same purpose. Our contribution consists in the implementation of a new assembler for the DNA fragment assembly problem capable of finding for the first time the optimal solutions for 20 out of 30 instances. The approach we proposed to adapt CSA for a discrete optimization problem is a novelty. We preserved the semantics of the original algorithm by applying standard operators from evolutionary algorithms. Following the same approach can make adapting new algorithms for discrete problems more accessible and more efficient compared to mapping algorithms designed for continuous optimization to combinatorial problems.