752 publications from this institution
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.
No abstract is provided for this article.
In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
The cuckoo search (CS) algorithm is a population-based algorithm, and its convergence is usually higher, even for potentially higher-dimensional problems. The algorithm is stable and robust, and has been applied in various applications. This chapter introduces the fundamentals of the standard cuckoo search algorithm with detailed descriptions of the main steps. Some demo codes in both Matlab and C++ are provided. Discussions of the efficient generation of L'evy flights are also given with examples.
No abstract is provided for this article.
Swarm intelligence is a very powerful technique appropriate to optimization. In this paper, we present a new swarm intelligence algorithm, which is based on the bat algorithm. Bat algorithm has been hybridized with differential evolution strategies. This hybridization showed very promising results on standard benchmark functions and also significantly improved the original bat algorithm.
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Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.
No abstract is provided for this article.
This paper presents an application of Discrete Cuckoo Search (DCS) algorithm for solving the Job Shop Scheduling Problem (JSSP), one of the most difficult NP-hard combinatorial optimization problems. DCS is recently developed by Ouaarab et al. in 2013, based on Cuckoo Search (CS), proposed by Yang and Deb in 2009. DCS performs its search via Lévy flights by proposing three different steps to move in the search space. It applies a search balance between local and global random walk. The performance of DCS is tested on a set of benchmarks of JSSP from the OR-Library. The test results are compared with a Particle Swarm Optimization (PSO) algorithm and they showed that DCS outperforms PSO.
It is now five years since the launch of the International Journal of Bio-Inspired Computation (IJBIC). At the same time, significant new progress has been made in the area of bio-inspired computation. This review paper summarises the success and achievements of IJBIC in the past five years, and also highlights the challenges and key issues for further research.
Multitemporal remote sensing image analysis is very important and thus requires trustable tools to analyze land cover changes. In this chapter, we present nature inspired techniques for flood damage evaluation using multitemporal MODIS data. The primary focus of the proposed approach is to combine the spectral and spatial features for the proper assessment of flood prone regions. Multitemporal MODIS data are analyzed in three stages: before, during, and after flood. Methodology involves two steps: in the first step, image clustering is carried out using the genetic algorithm, particle swarm optimization, and the firefly algorithm to distinguish water regions from nonwater using pixel-based spectral information. Most of the multimodal optimization problems are often solved by these three algorithms quite effectively. Subsequently, these clustered images are segmented using geometric features to extract water-covered regions. From the results obtained, a comprehensive performance evaluation of different algorithms is carried out, which leads to the conclusion that integrating region-based image segmentation along with clustering algorithms proves to be a very efficient tool for flood mapping.
This chapter introduces some important concepts about sequences, including simple arithmetic progression, geometric sequence, sum of sequences and series.
No abstract is provided for this article.
The pressure vessel design problem is a well-known design benchmark for validating bio-inspired optimisation algorithms. However, its global optimality is not clear and there has been no mathematical proof put forward. In this paper, a detailed mathematical analysis of this problem is provided that proves that 6,059.714335048436 is the global minimum. The Lagrange multiplier method is also used as an alternative proof and this method is extended to find the global optimum of a cantilever beam design problem.
No abstract is provided for this article.
