A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

/pdf/metaheuristics-from-design-to-implementation-1vqdfv8nk9.pdf

Metaheuristics: From Design to Implementation

Ambulance routing for disaster response with patient groups

We consider a routing problem for ambulances in a disaster response scenario, in which a large number of injured people require medical aid at the same time. The ambulances are used to carry medical personnel and patients. We distinguish two groups of patients: slightly injured people who can be assisted directly in the field, and seriously injured people who have to be brought to hospitals. Since ambulances represent a scarce resource in disaster situations, their efficient usage is of the utmost importance. Two mathematical formulations are proposed to obtain route plans that minimize the latest service completion time among the people waiting for help. Since disaster response calls for high-quality solutions within seconds, we also propose a Large Neighborhood Search metaheuristic. This solution approach can be applied at high frequency to cope with the dynamics and uncertainties in a disaster situation. Our experiments show that the metaheuristic produces near optimal solutions for a large number of test instances within very short response time. Hence, it fulfills the criteria for applicability in a disaster situation. Within the experiments, we also analysed the effect of various structural parameters of a problem, like the number of ambulances, hospitals, and the type of patients, on both running time of the heuristic and quality of the solutions.

Metaheuristics are truly diverse in nature—under the overarching theme of performing operations to escape local optima, algorithms as different as ant colony optimization, tabu search, harmony search, and genetic algorithms have emerged. Due to the unique functionality of each type of metaheuristic, comparison of metaheuristics is in many ways more difficult than other algorithmic comparisons. In this chapter, we discuss techniques for meaningful comparison of metaheuristics. We discuss how to create and classify instances in a new testbed and how to make sure other researchers have access to the problems for future metaheuristic comparisons. Further, we discuss the disadvantages of large parameter sets and how to measure complicating parameter interactions in a metaheuristic’s parameter space. Last, we discuss how to compare metaheuristics in terms of both solution quality and runtime.

/pdf/comparison-of-metaheuristics-27qw56kb9z.pdf

Comparison of Metaheuristics

Particle Swarm Optimization is a population-based method inspired by the social behaviour of individuals inside swarms in nature. Solutions of the problem are modelled as members of the swarm which fly in the solution space. The improvement of the swarm is obtained from the continuous movement of the particles that constitute the swarm submitted to the effect of inertia and the attraction of the members who lead the swarm. This work focuses on a recent Discrete Particle Swarm Optimization for combinatorial optimization, called Jumping Particle Swarm Optimization. Its effectiveness is illustrated on the minimum labelling Steiner tree problem: given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes, whose edges have the smallest number of distinct labels.

/pdf/discrete-particle-swarm-optimization-for-the-minimum-11i9wpk2ci.pdf

Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem

Given a connected, undirected graph G whose edges are labeled (or colored), the minimum labeling spanning tree (MLST) problem seeks a spanning tree on G with the minimum number of distinct labels (or colors). In recent work, the MLST problem has been shown to be NP-hard and an effective heuristic [maximum vertex covering algorithm (MVCA)] has been proposed and analyzed. We use a one-parameter genetic algorithm (GA) to solve the problem. In computational tests, the GA clearly outperforms MVCA.

A one-parameter genetic algorithm for the minimum labeling spanning tree problem

Worst-case behavior of the MVCA heuristic for the minimum labeling spanning tree problem

Given a connected, undirected graph G whose edges are labeled, the minimum label (or labeling) spanning tree (MLST) problem seeks a spanning tree on G with the minimum number of distinct labels. Maximum vertex covering algorithm (MVCA) is a well-known heuristic for the MLST problem. It is very fast and performs reasonably well. Recently, we developed a genetic algorithm (GA) for the MLST problem. The GA and MVCA are similarly fast but the GA outperforms the MVCA. In this paper, we present four modified versions of MVCA, as well as a modified GA. These modified procedures generate better results, but are more expensive computationally. The modified GA is the best performer with respect to both accuracy and running time

Improved Heuristics for the Minimum Label Spanning Tree Problem

Given a connected, undirected graph G whose edges are labeled (or colored), the colorful traveling salesman problem (CTSP) seeks a Hamiltonian tour of G with the minimum number of distinct labels (or colors). We prove that the CTSP is NP-complete and we present a heuristic algorithm and a genetic algorithm to solve the problem.

http://www.terpconnect.umd.edu/~bgolden/recent_presentation_pdfs_links/2007_jan_ctsp_ics.pdf

The Colorful Traveling Salesman Problem

The distribution of relief aid is a complex problem where the operations have to be managed efficiently due to limited resources. We present a routing problem for relief operations whose primary goal is to satisfy demand for relief supplies at many locations taking into account the urgency of each demand. We have a single vehicle of unlimited capacity. Each node (location) has a demand and a priority. The priority indicates the urgency of the demand. Typically, nodes with the highest priorities need to be visited before lower priority nodes. We describe a new and interesting model for humanitarian relief routing that we call the hierarchical traveling salesman problem (HTSP). We compare the HTSP and the classical TSP in terms of worst-case behavior. We obtain a simple, but elegant result that exhibits the fundamental tradeoff between efficiency (distance) and priority and we provide several related observations and theorems.

Yupei Xiong

Papers

A one-parameter genetic algorithm for the minimum labeling spanning tree problem

Worst-case behavior of the MVCA heuristic for the minimum labeling spanning tree problem

Improved Heuristics for the Minimum Label Spanning Tree Problem

The Colorful Traveling Salesman Problem

The hierarchical traveling salesman problem