752 publications from this institution
This first chapter intends to review and analyze the powerful new Harmony Search (HS) algorithm in the context of metaheuristic algorithms. I will first outline the fundamental steps of Harmony Search, and how it works. I then try to identify the characteristics of metaheuristics and analyze why HS is a good meta-heuristic algorithm. I then review briefly other popular metaheuristics such as par-ticle swarm optimization so as to find their similarities and differences from HS. Finally, I will discuss the ways to improve and develop new variants of HS, and make suggestions for further research including open questions.
The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose. These tools are combined with particle swarm optimization (PSO) and the selection of the best solutions depends on its fitness. Instead of the local best solution, a neighborhood of solutions for each particle can be defined and used for the calculation of the new position, based on the key ideas from semantic web domain. The preliminary results by optimizing ten benchmark functions showed the promising results and thus this method should be investigated further.
The flower pollination algorithm (FPA) was developed by Xin-She Yang in 2012, inspired by the flower pollination process of flowering plants. FPA has been extended to multi-objective optimization with promising results. This chapter provides an introduction to the flower pollination algorithm and its basic implementation.
This chapter introduces logistic regression, linear discriminant analysis, principal component analysis, singular value decomposition, and independent component analysis.
A common but challenging task in modelling geophysical and geological processes is to handle massive data and to minimize certain objectives. This can essentially be considered as an optimization problem, and thus many new efficient metaheuristic optimization algorithms can be used. In this paper, we will introduce some modern metaheuristic optimization algorithms such as genetic algorithms, harmony search, firefly algorithm, particle swarm optimization and simulated annealing. We will also discuss how these algorithms can be applied to various applications in earth sciences, including nonlinear least-squares, support vector machine, Kriging, inverse finite element analysis, and data-mining. We will present a few examples to show how different problems can be reformulated as optimization. Finally, we will make some recommendations for choosing various algorithms to suit various problems.
Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majority of metaheuristic algorithms, while efficiency analysis is equally challenging. In this paper, we intend to provide an overview of convergence and efficiency studies of metaheuristics, and try to provide a framework for analyzing metaheuristics in terms of convergence and efficiency. This can form a basis for analyzing other algorithms. We also outline some open questions as further research topics.
When calculating the connection between pipelines, aiming at the problems of large value of loss constraint, a small value of pressure, and inaccurate water hammer effect, the hydraulic calculation, and water hammer analysis process of large drop and long-distance municipal water transmission and distribution system are designed. The triangular curve grid processing pipeline is set as a grid structure from sparse to dense, and the normal imaging numerical relationship is used to control the pipeline constraint conditions Then, a long-distance water transmission and distribution system is set as a grid structure, and the large drop condition is defined. After the elevation value of the pipe centerline is calibrated by using the large drop hydraulic calculation algorithm, the numerical relationship of the water hammer effect is constructed to complete the process analysis. The test results show that the hydraulic calculation error is controlled at about 0.07%, the water level error is controlled at 0.1cm ~ 2cm, and the difference between the pressure value and the measured standard value is less than 2N.
Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm further to introduce chaos-enhanced firefly algorithm with automatic parameter tuning, which results in two more variants of FA. The author first compares the performance of these algorithms, and then uses them to solve a benchmark design problem in engineering. Results obtained by other methods will be compared and analyzed.
Metaheuristic optimization algorithms are usually based on swarm intelligence,and these algorithms are often referred to as smart algorithms.We review some of the widely used algorithms for optimization,including ant and bee algorithms,bat algorithm,cuckoo search, firefly algorithm and particle swarm optimization.We also discuss the challenging issues concerning parameter tuning and parameter control in metaheuristic algorithms.
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No abstract is provided for this article.
The increasing demand of high-speed and secure wireless broadband networks has generated significant interests in the optimisation and the analysis of energy-efficiency in orthogonal frequency division multiple access (OFDMA) system. In this study, the authors present an in-depth mathematical analysis of the maximisation of energy-efficiency by taking into consideration the quality of service (QoS). By using optimality conditions, they have shown that the optimal solutions can be obtained analytically. Furthermore, it has been proved in this study that this optimisation problem is strictly concave with the existence of a global maximum. Case studies with multiple subchannels validate the consistency of numerical results with the results obtained from derivative-free method like genetic algorithm. Graphical illustrations also validate and confirm the numerical values obtained from the mathematical analysis. Therefore the solution is optimal with respect to the OFDMA model adopted in this study. The authors proposed approach can be used for practical applications because of its simplicity and efficacy with QoS guaranteed for efficient energy consumption.
