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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Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints.Solving such optimization problems requires sophisticated algorithms and optimization techniques.A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA).Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved.Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms.Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms.After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms.We then test the unified GEM using 15 test benchmarks to validate its performance.Finally, further research topics are briefly discussed.
Many inverse problems in engineering can be considered as constrained optimisation, as the aim of inversion is to find the best parameter estimates so as to minimise the differences between the predicted results and the observations while satisfying all known constraints. Such optimisation problems can thus be solved by efficient optimisation techniques. As the number of degrees of freedom is usually very large, metaheuristic algorithms such as Cuckoo Search are particularly suitable for inverse problems, because metaheuristics are very efficient for solving non-linear global optimisation problems. In this paper, we will take a unified approach to inversion and optimisation and introduce a few nature-inspired metaheuristics, including genetic algorithms, differential evolution, firefly algorithm, Cuckoo Search, particle swarm optimisation and their applications in solving inverse problems.
Differential evolution is an optimization algorithm with vector-based mutation and crossover. This chapter introduces the principle of differential evolution and its variants. A demo implementation will be provided with discussions.
No abstract is provided for this article.
No abstract is provided for this article.
Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks.Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark.The outstanding performance and efficiency of swarm-based algorithms inspired many new developments, though mathematical understanding of metaheuristics remains partly a mystery.In contrast to the classic deterministic algorithms, metaheuristics such as PSO always use some form of randomness, and such randomization now employs various techniques.This paper intends to review and analyze some of the convergence and efficiency associated with metaheuristics such as firefly algorithm, random walks, and Lévy flights.We will discuss how these techniques are used and their implications for further research.
The application of optimization and data mining in databases in geosciences is becoming promising, though still at an early stage. We present a case study of the application of data mining and optimization in the prediction of fractures using well-logging data. We compare various approaches, including multiple regression analysis (MRA), back-propagation neural network (BPNN), and support vector machine (SVM). The modelling problem in data mining is formulated as a minimization problem, showing that we can reduce an 8-D problem to a 4-D problem by dimension reduction. The MRA, BPNN and SVM methods are used as optimization techniques for knowledge discovery in data. The calculations for both the learning samples and prediction samples show that both BPNN and SVM can have zero residuals, which suggests that these combined data-mining techniques are practical and efficient.
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No abstract is provided for this article.
No abstract is provided for this article.
Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.
No abstract is provided for this article.
No abstract is provided for this article.
This chapter introduces a powerful tool for classification and regression, including linear support vector machines, nonlinear support vector machines, and support vector regression.
The traveling salesman problem (TSP) is one of the most studied problems in computational intelligence and operations research. Since its first formulation, a myriad of works has been published proposing different alternatives for its solution. Additionally, a plethora of advanced formulations have also been proposed by the related practitioners, trying to enhance the applicability of the basic TSP. This chapter is firstly devoted to providing an informed overview on the TSP. For this reason, we first review the recent history of this research area, placing emphasis on milestone studies contributed in recent years. Next, we aim at making a step forward in the field proposing an experimentation hybridizing three different reputed bio-inspired computational metaheuristics (namely, particle swarm optimization, the firefly algorithm, and the bat algorithm) and the novelty search mechanism. For assessing the quality of the implemented methods, 15 different datasets taken from the well-known TSPLIB have been used. We end this chapter by sharing our envisioned status of the field, for which we identify opportunities and challenges which should stimulate research efforts in years to come.
