Publications

Preface

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Genetic Algorithms

Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms

No abstract is provided for this article.

Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect

The growing costs of fuel and operation of power generating units warrant improvement of optimization methodologies for economic dispatch (ED) problems. The practical ED problems have non-convex objective functions with equality and inequality constraints that make it much harder to find the global optimum using any mathematical algorithms. Modern optimization algorithms are often meta-heuristic, and they are very promising in solving nonlinear programming problems. This paper presents a novel approach to determining the feasible optimal solution of the ED problems using the recently developed Firefly Algorithm (FA). Many nonlinear characteristics of power generators, and their operational constraints, such as generation limitations, prohibited operating zones, ramp rate limits, transmission loss, and nonlinear cost functions, were all contemplated for practical operation. To demonstrate the efficiency and applicability of the proposed method, we study four ED test systems having non-convex solution spaces and compared with some of the most recently published ED solution methods. The results of this study show that the proposed FA is able to find more economical loads than those determined by other methods. This algorithm is considered to be a promising alternative algorithm for solving the ED problems in practical power systems.

Color Image Segmentation By Cuckoo Search

In this paper, a clustering based color image segmentation technique is proposed and the clustering technique is optimized by the cuckoo search method. The proposed approach consists of two phase segmentation processes. In the first phase, cluster centres are optimized by using the cuckoo search algorithm and in the second phase, empty and frequent clutters are removed and merged according to pre-defined rules. This cluster centre based clustering technique is then used to find the optimum centre within a cluster, while cuckoo search is applied to find the optimum cluster centre for each segment in the image. Comparison of the proposed method is performed with the genetic algorithm (GA), dynamic control particle swarm optimization (DCPSO) algorithm and firefly algorithm based color image segmentation methods over five benchmark color images. The parameters of the proposed method are tuned through empirical testing. Results demonstrated that the proposed method can be an effective tool for image segmentation.

Exponentials and Logarithms

Exponentials and logarithms are important basic mathematical functions. This chapter introduces the basic exponential and logarithmic functions and their properties.

Bio-Inspired Approaches in Telecommunications

Bio-inspired algorithms are modern optimization tools that are capable of solving complex design problems in many applications. Such algorithms aim to speed up the optimization process so as to tackle tougher optimization problems. Some of these algorithms, such as particle swarm optimization and cuckoo search, have been found to be much more feasible and practical in obtaining the optimal solution, compared to conventional mathematical methods. In this chapter, we will review design problems and their solution methods concerning resource and power allocations in orthogonal frequency division multiple access systems.

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.

Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism

No abstract is provided for this article.

Nature-Inspired Optimization Algorithms

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Optimal test sequence generation using firefly algorithm

Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths.

Computational optimization, modelling and simulation: Recent advances and overview

Computational optimization is becoming increasingly important in engineering design and industrial applications. Products and services are often concerned with the maximization of profits and reduction of cost, but also aim at being more energy-efficient, environment-friendly and safety-ensured; at the same time they are limited by resources, time and money. This second workshop on Computational Optimization, Modelling and Simulation (COMS 2011) at ICCS 2011 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry.

A Multiscale Simulation Framework to Investigate Hydrobiogeochemical Processes in the Groundwater-Surface Water Interaction Zone

Research Progress on Iron-Heart <i>Cunninghamia lanceolate</i>

Cunninghamia lanceolate (Lambert.) Hooker is one of the main fast-growing timber forest species in southern China which has a long history of cultivation and spreads across 28 provinces, cities, and regions. Recently, a variant of fir was discovered in the Xiaoxi National Nature Reserve in Hunan Province. The heartwood is hard as iron and its ratio is more than 80%, with the especial character of anti-corruption. It is a natural germplasm resource, called Iron-heart Cunninghamia lanceolate. Study on it is still in the stage of data accumulation. In this paper, we studied it from three points as follows: (1) Plus tree selection and construction of germplasm resources nursery. (2) Study on cone and seed quality. (3) Genetic structure analysis of natural population. The research of Iron-heart Cunninghamia lanceolate lays a theoretical foundation for the protection, development, and utilization of the black-heart wood germplasm resources of Iron-heart Cunninghamia lanceolate in the future.

Modelling of fast flame–shock wave interactions with a variable piston speed

The interaction of a shock wave with an existing fast flame (convection–reaction driven) is considered whereby the driving piston is not at a constant speed. Between the shock and the flame, there is an induction zone which governs the acoustic coupling between the flame and the shock. The asymptotic matching of this zone to the regions near the flame is presented. The case of a variable piston speed is allowed so that pulsed transient inputs to fast deflagrations can be simulated.

Computational Optimization and Applications in Engineering and Industry

No abstract is provided for this article.

Random Walks and Optimization

Randomization is widely used in nature-inspired optimization algorithms, and random walks are a form of randomization. This chapter introduces the basic concepts of random walks, Lévy flights and Markov chains as well as their links with optimization algorithms.

A bio-inspired algorithm for identification of critical components in the transportation networks

Critical components in a transportation or communication network are those which should be better protected or secured because their removal has a significant impact on the whole network. In such networks, they will be congested if they are being offered more traffic than it can process. In this paper, we employ principles of slime mould Physarum polycephalum foraging behaviour to identify the critical components in congested networks. When Physarum colonises a substrate, it develops a network of protoplasmic tube aimed at transporting nutrients and metabolites between distance parts of the cell. The protoplasmic network is continuously updating to minimize the transportation time, maximize the amount of cytoplasm pumped and minimize the overall length of the network. This optimization is achieved via a positive feedback between flux of cytoplasm and tube diameters. When a segment of a protoplasmic network is removed, the whole network reconfigures and thickness of tubes is updated till an equilibrium state is reached. The transient period from a disturbed state to an equilibrium state shows how critical the removed segment was. We develop a Physarum-inspired algorithm to identify critical links or nodes in a network by removing them from the network or calculating the transient period to new equilibrium state. The efficiency of the proposed method are demonstrated in numerical examples.