Belaïd Ahiod

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.

Improved Ant Colony Optimization Routing Protocol for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of autonomous nodes, deployed to monitor various environments (even under hostility). Major challenges arise from its limited energy, communication failures and computational weakness. Many issues in WSNs are formulated as NP-hard optimization problems, and approached through metaheuristics. This paper outlines an Ant Colony Optimization (ACO) used to solve routing problems in WSNs. We have studied an approach based on ACO. So, we designed an improved one that reduces energy consumption and prolongs WSN lifetime. Through simulation results, our proposal efficiency is validated.

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.

Particle Swarm Optimization Compared to Ant Colony Optimization for Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are an emerging technology that used to monitor various environments. Despite of WSN advantages, it suffers from intrinsic limitations related to communication failures, computational weaknesses and limited energy. Hence, many challenges are considered as NP-hard optimization problems, and resolved by metaheuristics. This paper, proposes a routing approach based on Particle Swarm Optimization (PSO). Compared to the ACO approach, PSO reduces the energy consumption and extends the life of WSN. Through performing many experimentations the PSO efficiency is validated.

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.

Impact of Random Waypoint Mobility Model on Ant-based Routing Protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) consist of static or mobile nodes, depending on the application requirements. The weaknesses of sensors node with mobility present major challenges especially in routing. In many cases, routing in WSNs is considered as NP-hard optimization problem that needs efficient and robust methods such as metaheuristics. Ant colony optimization (ACO) is one of the most used meta-heuristics for solving this problem in static WSN. For dynamic WSN, the random waypoint (RWP) model is widely proposed to evaluate the performance of mobile networks. In this paper, we present a new evaluation of the RWP mobility model impact on ant-based routing protocol for WSN, using different parameters like the number of user nodes, simulation time and simulation area. Further, this study estimates the performance of the ACO routing protocol for mobile WSNs.

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.

Energy efficient teaching-learning-based optimization for the discrete routing problem in wireless sensor networks

Wireless sensor networks (WSNs) are composed of sensor nodes, having limited energy resources and low processing capability. Accordingly, major challenges are involved in WSNs Routing. Thus, in many use cases, routing is considered as an NP-hard optimization problem. Many routing protocols are based on metaheuristics, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Despite the fact that metaheuristics have provided elegant solutions, they still suffer from complexity concerns and difficulty of parameter tuning. In this paper, we propose a new routing approach based on Teaching Learning Based Optimization (TLBO) which is a recent and robust method, consisting on two essential phases: Teacher and Learner. As TLBO was proposed for continuous optimization problems, this work presents the first use of TLBO for the discrete problem of WSN routing. The approach is well founded theoretically as well as detailed algorithmically. Experimental results show that our approach allows obtaining lower energy consumption which leads to a better WSN lifetime. Our method is also compared to some typical routing methods; PSO approach, advanced ACO approach, Improved Harmony based approach (IHSBEER) and Ad-hoc On-demand Distance Vector (AODV) routing protocol, to illustrate TLBO’s routing efficiency.