The metaheuristic called the ant colony method has been inspired by entomology, the science of insect behavior. An interesting observation is that ants are apparently capable of solving what we would call optimization problems, such as finding a short path between the nest and a source of food. This result will be discussed in detail in Section 5.2. The ants’ ability to collectively find optimal or nearly optimal solutions for their development and survival is witnessed by their biomass, which is estimated to be similar to that of humans.

In contrast with exact algorithms whose worst-case time complexity is known (see Chapter 1), metaheuristics do not provide that kind of bound. They can be very effective on a given instance of a problem and, at the same time, show long running times on another without finding a satisfactory solution. On the other hand, for example, the selection sort algorithm could spend different amount of time on an already sorted list, and on a list sorted in the opposite order, but we know that, on any list permutation, its time complexity function T(n) will be bounded by a seconddegree polynomial and the list will be sorted correctly.

The goal of this chapter, which is based on reference [63], is to better characterize the nature of the search space of particular problems where the number of optimal solutions, and the difficulty of finding them, varies as a function of a parameter that can be modified at will. According to the value of the parameter, the system goes from a situation in which there are many solutions to the problem to a situation in which, suddenly, there are no solutions at all. This type of behavior is typical of phase transitions in physics and the term has been adopted in the computational field by analogy with the physical world.

We study the capability of connected autonomous vehicles to reduce traffic congestion in the case of a forced lane merging, due, for instance, to road construction. To prevent the formation of a jammed phase, we propose to organize the vehicles before the merging point. Cooperative behaviors among neighboring autonomous vehicles, although local, can produce emergent properties that have to be understood to control their benefit at a large traffic scale. In this paper, we show that in a restricted scenario, the resulting collective effects may be positive as well as negative. On the one hand, by properly reducing the speed before the bottleneck, we can obtain a merging process that maintains a synchronized traffic regime. However, the location of the merging front in a two-lane to a one-lane traffic reduction is shown to be a key element to adjust the parameters of our local merging algorithm. At a macroscopic scale, the goal is to keep the lane merging front at the same road location to ensure its effectiveness and stability over time. Other performance metrics of this merging algorithm are also derived. They reveal important features that are expected to occur with any merging algorithms for autonomous vehicles.

In this chapter we shall present some newer metaheuristics for optimization that are loosely based on analogies with the biological world. In contrast with the metaheuristics described in the previous chapters, these algorithms have been around for a comparatively short time and it is difficult to know whether they are going to be as successful as more classical methods. Be that as it may, these metaheuristics contain some new elements that make them worth knowing.

Metaheuristics are a family of algorithmic techniques that are useful for solving difficult problems. Roughly speaking, the difficulty or hardness of a problem is the quantity of computational resources needed to find the solution. When this quantity increases at a high rate with increasing problem size, in a way that will be defined precisely later, we are facing a difficult problem. The theory of the computational complexity of algorithmic problems is well known [34, 66] and, in this first chapter, we shall look at the basics and the main conclusions since these ideas are needed to understand the place of metaheuristics in this context.

This textbook focuses on the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems and is suitable for undergraduate & graduate students, researchers, and professionals in computer science, engineering, and logistics.

Platelets are small blood particles that play a fundamental role in various physiological processes such as, for instance, the formation of clots that prevent bleeding. Their successful action depends on their adhesion and aggregation capabilities, as well as their transport properties in blood, that is their presence at a vessel wall. Their movement being affected by the shape and deformability of red blood cells within the blood flow, an accurate description of platelets transport is very challenging. Currently, medical devices aimed at the determination of platelets function often fail to detect platelets disorder. Here, we combine discrete numerical models, high performance computing, Bayesian computation with in vitro experiments to infer platelets properties for healthy and unhealthy patients.

In this paper we suggest a simple algorithm for merging the traffic of a main road with on-ramp traffic for collaborative Connected and Autonomous Vehicles (CAVs). The main asset of the algorithm being that most values can be formally computed, we quantitatively express the global impact of the merging strategy on the input and output flows of the merging area. For instance, how platoons emerge, their lengths, the space between them and their traffic patterns. We refer to the literature for all local aspects of synchronisation of cars. Here we simply assume that cars can execute the merging algorithm which requires that the cars are aware of a sequence number and operates following the sequence. Our analysis focuses on saturated flows and the control of collective behavior.

Circulatory models can significantly help develop new ways to alleviate the burden of stroke on society. However, it is not always easy to know what hemodynamics conditions to impose on a numerical model or how to simulate porous media, which ineluctably need to be addressed in strokes. We propose a validated open-source, flexible, and publicly available lattice-Boltzmann numerical framework for such problems and present its features in this chapter. Among them, we propose an algorithm for imposing pressure boundary conditions. We show how to use the method developed by Walsh et al. (Comput Geosci 35(6):1186-1193, 2009) to simulate the permeability law of any porous medium. Finally, we illustrate the features of the framework through a thrombolysis model.

The proposed cryptographic model, VHCA, is a novel approach to encryption using cellular automata. This block cipher algorithm enables encryption and decryption of a plaintext using an arrangement of radius1 CA toggle rules, expressed as a binary secret key, to encrypt blocks having 128-bits or more. While hybrid CA forward evolution is used for encryption, a deterministic preimage computation logic is used for decryption. Promising results were obtained using periodic boundary condition, which favors diffusion, in combination with multiple permutive rules that are inherently balanced functions. Evaluations of the method using statistical randomness test suites, NIST and PractRand, point to the cryptographic robustness of VHCA.

In this paper we suggest a simple algorithm for merging the traffic of a main road with on-ramp traffic. We consider collaborative Connected and Autonomous Vehicles (CAVs). We are interested in the global impact of the merging strategy on the input and output flows of the merging area. We refer to the literature for all local aspects of synchronisation of cars. Our analysis focuses on saturated flows and the control of collective behaviour.

We consider the car following problem for a set of autonomous vehicles following each other on either an infinite or circular road. The behavior of each car is specified by its "speed regulator", a device that decides to increase or decrease the speed of the car as a function of the head-tail distance to its predecessor and the speed of both cars. A collective behavior emerges that corresponds to previously proposed cellular automata traffic models. We further analyze the traffic patterns of the system in the long term, as governed by the speed regulator and we study under which conditions traffic patterns of maximum flow can or cannot be reach. We show the existence of suboptimal flow conditions that require external coordination mechanisms (that we do not consider in this paper) in order to reach the optimal flow achievable with the given density. In contrast with other approaches, we do not try to reproduce observed or measured traffic patterns. We analyze a deterministic speed regulator in order to decipher the emergent dynamics, and to ponder what maneuvers can be safely performed. Here, we restrict our attention to the car following problem. By comparing our speed regulator with classical models, auch as the Nagel–Schreckenberg and KKW models, we observe that although our regulator is formulated in simple terms, its dynamics share similarities with these models. In particular, the KKW model is designed to reproduce the observed behavior that a trailing car in the synchronization range of the leading car tends to regulate its speed to maintain a constant distance. this same behavior is adopted by our speed regulator, showing that this is a safe way of driving.

One of the routine clinical treatments to eliminate ischemic stroke thrombi is injecting a biochemical product into the patient’s bloodstream, which breaks down the thrombi’s fibrin fibers: intravenous or intravascular thrombolysis. However, this procedure is not without risk for the patient; the worst circumstances can cause a brain hemorrhage or embolism that can be fatal. Improvement in patient management drastically reduced these risks, and patients who benefited from thrombolysis soon after the onset of the stroke have a significantly better 3-month prognosis, but treatment success is highly variable. The causes of this variability remain unclear, and it is likely that some fundamental aspects still require thorough investigations. For that reason, we conducted in vitro flow-driven fibrinolysis experiments to study pure fibrin thrombi breakdown in controlled conditions and observed that the lysis front evolved non-linearly in time. To understand these results, we developed an analytical 1D lysis model in which the thrombus is considered a porous medium. The lytic cascade is reduced to a second-order reaction involving fibrin and a surrogate pro-fibrinolytic agent. The model was able to reproduce the observed lysis evolution under the assumptions of constant fluid velocity and lysis occurring only at the front. For adding complexity, such as clot heterogeneity or complex flow conditions, we propose a 3-dimensional mesoscopic numerical model of blood flow and fibrinolysis, which validates the analytical model’s results. Such a numerical model could help us better understand the spatial evolution of the thrombi breakdown, extract the most relevant physiological parameters to lysis efficiency, and possibly explain the failure of the clinical treatment. These findings suggest that even though real-world fibrinolysis is a complex biological process, a simplified model can recover the main features of lysis evolution.