Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Suboptimal feedback control of PDEs by solving HJB equations on adaptive sparse grids

International audienceAn approach to solve finite time horizon suboptimal feedback control problems for partial differential equations is proposed by solving dynamic programming equations on adaptive sparse grids. The approach is illustrated for the wave equation and an extension to equations of Schrödinger type is indicated. A semi-discrete optimal control problem is introduced and the feedback control is derived from the corresponding value function.The value function can be characterized as the solution of an evolutionary Hamilton-Jacobi Bellman (HJB) equation which is defined over a state space whose dimension is equal to the dimension of the underlying semi-discrete system. Besides a low dimensional semi-discretization it is important to solve the HJB equation efficiently to address the curse of dimensionality.We propose to apply a semi-Lagrangian scheme using spatially adaptive sparse grids. Sparse grids allow the discretization of the value functions in (higher) space dimensions since the curse of dimensionality of full grid methods arises to a much smaller extent. For additional efficiency an adaptive grid refinement procedure is explored.We present several numerical examples studying the effect the parameters characterizing the sparse grid have on the accuracy of the value function and the optimal trajectory

An evolutionary behavioral model for decision making

For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud the same evolutionary mechanism is able to assemble quite\ud complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies

Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure