Publications

Metaheuristic Optimization: Algorithm Analysis and Open 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.

Free lunch or no free lunch: that is not just a question?

The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in algorithm analysis and performance evaluations. No-free-lunch theorems are of both theoretical and practical importance, while many important studies on convergence analysis of various metaheuristic algorithms have proven to be fruitful. This paper discusses the recent results on no-free-lunch theorems and algorithm convergence, as well as their important implications for algorithm development in practice. Free lunches may exist for certain types of problem. In addition, we will highlight some open problems for further research.

Introduction to Computational Mathematics

Mathematical Foundations Algorithmic Complexity Ordinary Differential Equations Partial Differential Equations Roots of Nonlinear Equations Numerical Integration Computational Linear Algebra Interpolation Finite Difference Methods for ODEs Finite Difference Methods for PDEs Finite Volume Method Finite Element Method Mathematical Optimization Mathematical Programming Stochastic Models Data Modeling Metaheuristic Methods Bee Algorithms Swarm Optimization.

Chaos in small-world networks

A nonlinear small-world network model has been presented to investigate the effect of nonlinear interaction and time delay on the dynamic properties of small-world networks. Both numerical simulations and analytical analysis for networks with time delay and nonlinear interaction show chaotic features in the system response when nonlinear interaction is strong enough or the length scale is large enough. In addition, the small-world system may behave very differently on different scales. Time-delay parameter also has a very strong effect on properties such as the critical length and response time of small-world networks.

Optimization of CFRP strengthening of precracked structures using genetic algorithms

A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduling Problems

Hybrid flowshop scheduling problems include the generalization of flowshops with parallel machines in some stages. Hybrid flowshop scheduling problems are known to be NP-hard. Hence, researchers have proposed many heuristics and metaheuristic algorithms to tackle such challenging tasks. In this letter, a recently developed discrete firefly algorithm is extended to solve hybrid flowshop scheduling problems with two objectives. Makespan and mean flow time are the objective functions considered. Computational experiments are carried out to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm outperforms many other metaheuristics in the literature.

Review and Applications of Metaheuristic Algorithms in Civil Engineering

No abstract is provided for this article.

A hybrid bat algorithm

Swarm intelligence is a very powerful technique to be used for optimization purposes. In this paper we present a new swarm intelligence algorithm, based on the bat algorithm. The Bat algorithm is hybridized with differential evolution strategies. Besides showing very promising results of the standard benchmark functions, this hybridization also significantly improves the original bat algorithm.

Social Algorithms and Optimization

No abstract is provided for this article.

A Brief Review of Nature-Inspired Algorithms for Optimization

Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very efficient and thus have become popular tools for solving real-world problems. Some algorithms are insufficiently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.

Analysis of Algorithms

Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this chapter, we carry out a critical analysis of these SI-based algorithms and other nature-inspired algorithms by analyzing the ways they mimic evolutionary operators. We also analyze ways of achieving exploration and exploitation in algorithms by using mutation, crossover, and selection. In addition, we study these algorithms using dynamic systems, self-organization, and the Markov chain framework. Finally, we provide some discussion and topics for further research.

Bio-Inspired Computation: Success and Challenges of IJBIC

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 summarizes the success and achievements of IJBIC in the past five years, and also highlights the challenges and key issues for further research.

A mathematical model for voigt poro‐visco‐plastic deformation

A mathematical model for poro-visco-plastic compaction and pressure solution in porous sediments has been formulated using the Voigt-type rheological constitutive relation as derived from experimental data. The governing equations reduce to a nonlinear hyperbolic heat conduction equation in the case of slow deformation where permeability is relatively high and the pore fluid pressure is nearly hydrostatic, while travelling wave exists in the opposite limit where over-pressuring occurs and the pore fluid pressure is almost quasi-lithostatic. Full numerical simulation using a finite element method agree well with the approximate analytical solutions.

Path Optimization for Software Testing: An Intelligent Approach using Cuckoo Search.

No abstract is provided for this article.

Sensitivity analysis, optimisation, and sampling methods applied to continuous models.

This report describes the initial stages of work for the Software Support for Metrology programme project Tools for Continuous Modelling and Simulation. The aims of the project are to improve the understanding and use of continuous modelling packages and to build the confidence of model users in the quality of their results. We introduce the study of sensitivity analysis, optimisation, and sampling methods applied to nite element models. A key problem affecting the application of such techniques to continuous models and black box software is how to gain the most accurate information on the performance and sensitivity of the modelled system using the least amount of computational effort. Three small finite element models were developed that cover a range of physical problems, complexities and interactions between model parameters. The models are introduced and we explain the mathematical background to the methods that were applied to analyse the problems, and summarise our main conclusions and recommendations.

Discrete event simulation

This chapter introduces the fundamentals of discrete event simulation, including basic notations, queue setting, and queueing models. Queue model components and assumptions are given in detail, and mathematical models for interarrivals and service-time distributions are explained. Both the single-server and multiserver models are presented, and application of queue management is also covered with some simple examples.

Introduction to Algorithms

Cuckoo search for business optimization applications

Cuckoo search has become a popular and powerful metaheuristic algorithm for global optimization. In business optimization and applications, many studies have focused on support vector machine and neural networks. In this paper, we use cuckoo search to carry out optimization tasks and compare the performance of cuckoo search with support vector machine. By testing benchmarks such as project scheduling and bankruptcy predictions, we conclude that cuckoo search can perform better than support vector machine.