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

Clustering in sensor networks: A literature survey

Wireless sensor networks (WSNs) have recently gained the attention of researchers in many challenging aspects. The most important challenge in these networks is energy conservation. One of the most popular solutions in making WSNs energy-efficient is to cluster the networks. In clustering, the nodes are divided into some clusters and then some nodes, called cluster-heads, are selected to be the head of each cluster. In a typical clustered WSN, the regular nodes sense the field and send their data to the cluster-head, then, after gathering and aggregating the data, the cluster-head transmits them to the base station. Clustering the nodes in WSNs has many benefits, including scalability, energy-efficiency, and reducing routing delay. In this paper we present a state-of-the-art and comprehensive survey on clustering approaches. We first begin with the objectives of clustering, clustering characteristics, and then present a classification on the clustering algorithms in WSNs. Some of the clustering objectives considered in this paper include scalability, fault-tolerance, data aggregation/fusion, increased connectivity, load balancing, and collision avoidance. Then, we survey the proposed approaches in the past few years in a classified manner and compare them based on different metrics such as mobility, cluster count, cluster size, and algorithm complexity

On the landscape of combinatorial optimization problems

This paper carries out a comparison of the fitness landscape for four classic optimization problems: Max-Sat, graph-coloring, traveling salesman, and quadratic assignment. We have focused on two types of properties, local average properties of the landscape, and properties of the local optima. For the local optima we give a fairly comprehensive description of the properties, including the expected time to reach a local optimum, the number of local optima at different cost levels, the distance between optima, and the expected probability of reaching the optima. Principle component analysis is used to understand the correlations between the local optima. Most of the properties that we examine have not been studied previously, particularly those concerned with properties of the local optima. We compare and contrast the behavior of the four different problems. Although the problems are very different at the low level, many of the long-range properties exhibit a remarkable degree of similarity