No abstract is provided for this article.
Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimisation problems. In this paper, we show how to use the recently developed firefly algorithm to solve non-linear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in the literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.
The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem.
No abstract is provided for this article.
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.
No abstract is provided for this article.
No abstract is provided for this article.
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.
Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently.
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.
Particle swarm optimization (PSO) is a swarm intelligence based algorithm and is among the most widely used nature-inspired algorithms. This chapter introduces the standard PSO, its variants and demo implementation.
In computer science, a computational challenge exists in finding a globally optimized solution from a tremendously large search space. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods have been extensively studied in the past, and progressively extended in order to suit a wide range of optimization problems. Researchers recently have invented a collection of heuristic optimization methods inspired by the movements of animals and insects (e.g., Firefly, Cuckoos, Bats and Accelerated PSO) with the advantages of efficient computation and easy implementation. This paper proposes a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) that imitates the way wolves search for food and survive by avoiding their enemies. The contribution of the paper is twofold: 1. for verifying the efficacy of the WSA the algorithm is tested quantitatively and compared to other heuristic algorithms under a range of popular non-convex functions used as performance test problems for optimization algorithms; 2. The WSA is investigated with respective to its memory requirement. Superior results are observed in most tests.
The firefly algorithm (FA) is a swarm intelligence based algorithm for optimization. FA is a nonlinear system that uses attraction to evolve its multi-agent system. This chapter introduces the basic ideas of FA and its variants. A demo implementation is also provided.
This short discussion concerns some inaccuracy in the recently published paper “M. Asadi et al. Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm. Applied thermal engineering, 2014”. There was a mix-up between the cuckoo search (CS) with the cuckoo optimization algorithm (COA), which may cause further confusion if uncorrected. In this discussion, we intend to clarify the differences between both algorithms so as to provide a more accurate description of both algorithms.
Copyright © 2014 Xin-She Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
