Showing papers in "Swarm and evolutionary computation in 2011"

A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

Abstract: a b s t r a c t The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures - independence, normality, and homoscedasticity - yields to nonparametric ones the task of performing a rigorous comparison among algorithms. In this paper, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis. The test problems of the CEC'2005 special session on real parameter optimization will help to illustrate the use of the tests throughout this tutorial, analyzing the results of a set of well-known evolutionary and swarm intelligence algorithms. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results.

Multiobjective evolutionary algorithms: A survey of the state of the art

Abstract: A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.

Surrogate-assisted evolutionary computation: Recent advances and future challenges

Abstract: Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.

Constraint-Handling in Nature-Inspired Numerical Optimization: Past, Present and Future

Abstract: In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem. Nowadays, however, there exists a considerable amount of research devoted to design techniques for handling constraints within a nature-inspired algorithm. This paper presents an analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms. From them, the most popular approaches are analyzed in more detail. For each of them, some representative instantiations are further discussed. In the last part of the paper, some of the future trends in the area, which have been only scarcely explored, are briefly discussed and then the conclusions of this paper are presented.

Parameter tuning for configuring and analyzing evolutionary algorithms

Abstract: In this paper we present a conceptual framework for parameter tuning, provide a survey of tuning methods, and discuss related methodological issues. The framework is based on a three-tier hierarchy of a problem, an evolutionary algorithm (EA), and a tuner. Furthermore, we distinguish problem instances, parameters, and EA performance measures as major factors, and discuss how tuning can be directed to algorithm performance and/or robustness. For the survey part we establish dierent taxonomies to categorize tuning methods and review existing work. Finally, we elaborate on how tuning can improve methodology by facilitating well-funded experimental comparisons and algorithm analysis.

Clustering using firefly algorithm: Performance study

Abstract: A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, that simulates the flash pattern and characteristics of fireflies. Clustering is a popular data analysis technique to identify homogeneous groups of objects based on the values of their attributes. In this paper, the FA is used for clustering on benchmark problems and the performance of the FA is compared with other two nature inspired techniques — Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and other nine methods used in the literature. Thirteen typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques. From the results obtained, we compare the performance of the FA algorithm and conclude that the FA can be efficiently used for clustering.

An introduction and survey of estimation of distribution algorithms

Abstract: Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. This explicit use of probabilistic models in optimization offers some significant advantages over other types of metaheuristics. This paper discusses these advantages and outlines many of the different types of EDAs. In addition, some of the most powerful efficiency enhancement techniques applied to EDAs are discussed and some of the key theoretical results relevant to EDAs are outlined.

Real-parameter evolutionary multimodal optimization — A survey of the state-of-the-art

Abstract: Multimodal optimization amounts to finding multiple global and local optima (as opposed to a single solution) of a function, so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable one while still maintaining the optimal system performance. Evolutionary Algorithms (EAs), due to their population-based approaches, are able to detect multiple solutions within a population in a single simulation run and have a clear advantage over the classical optimization techniques, which need multiple restarts and multiple runs in the hope that a different solution may be discovered every run, with no guarantee however. Numerous evolutionary optimization techniques have been developed since late 1970s for locating multiple optima (global or local). These techniques are commonly referred to as “niching” methods. Niching can be incorporated into a standard EA to promote and maintain formation of multiple stable subpopulations within a single population, with an aim to locate multiple globally optimal or suboptimal solutions simultaneously. This article is the first of its kind to present a comprehensive review of the basic concepts related to real-parameter evolutionary multimodal optimization, a survey of the major niching techniques, a detailed account of the adaptation of EAs from diverse paradigms to tackle multimodal problems, benchmark problems and performance measures.

Optimal placement of multi-distributed generation units including different load models using particle swarm optimization

Abstract: This paper proposes a multi-objective index-based approach for optimally determining the size and location of multi-distributed generation (multi-DG) units in distribution systems with different load models. It is shown that the load models can significantly affect the optimal location and sizing of DG resources in distribution systems. The proposed multi-objective function to be optimized includes a short circuit level parameter to represent the protective device requirements. The proposed function also considers a wide range of technical issues such as active and reactive power losses of the system, the voltage profile, the line loading, and the Mega Volt Ampere (MVA) intake by the grid. An optimization technique based on particle swarm optimization (PSO) is introduced. An analysis of the continuation power flow to determine the effect of DG units on the most sensitive buses to voltage collapse is carried out. The proposed algorithm is tested using a 38-bus radial system and an IEEE 30-bus meshed system. The results show the effectiveness of the proposed algorithm.

Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks

Abstract: The main challenges in designing and planning the operations of Wireless Sensor Networks (WSNs) are to optimize energy consumption and prolong network lifetime. Cluster-based routing techniques, such as the well-known low-energy adaptive clustering hierarchy (LEACH), are used to achieve scalable solutions and extend the network lifetime until the last node dies (LND). Also, evolutionary algorithms (EAs), have been successfully used in recent years as meta-heuristics to address energy-aware routing challenges by designing intelligent models that collaborate together to optimize an appropriate energy-aware objective function. On the other hand, some protocols, such as stable election protocol (SEP), are concerned with another objective: extending the stability time until the first node dies (FND). Often, there is a tradeoff between extending the time until FND and the time until LND. To our knowledge, no attempt has been made to obtain a better compromise between the stability time and network lifetime. This paper reformulates the design of the most important characteristic of the EA (i.e., the objective function), so as to obtain a routing protocol that can provide more robust results than the existing heuristic and meta-heuristic protocols in terms of network stability period, lifetime, and energy consumption. An evolutionary-based routing protocol is proposed, which can guarantee better tradeoff between the lifespan and the stability period of the network with efficient energy utilization. To support this claim, extensive simulations on 90 homogeneous and heterogeneous WSN models are evaluated and compared against the LEACH, SEP, and one of the existing evolutionary-based routing protocols, hierarchical clustering-algorithm-based genetic algorithm (HCR).

Adapted ant colony optimization for efficient reconfiguration of balanced and unbalanced distribution systems for loss minimization

Abstract: This paper presents an efficient method for the reconfiguration of radial distribution systems for minimization of real power loss using adapted ant colony optimization. The conventional ant colony optimization is adapted by the graph theory to always create feasible radial topologies during the whole evolutionary process. This avoids tedious mesh check and hence reduces the computational burden. The initial population is created randomly and a heuristic spark is introduced to enhance the pace of the search process. The effectiveness of the proposed method is demonstrated on balanced and unbalanced test distribution systems. The simulation results show that the proposed method is efficient and promising for reconfiguration problem of radial distribution systems.

Tuning of neural networks using particle swarm optimization to model MIG welding process

Abstract: Particle swarm optimization technique has been used for tuning of neural networks utilized for carrying out both forward and reverse mappings of metal inert gas (MIG) welding process. Four approaches have been developed and their performances are compared to solve the said problems. The first and second approaches deal with tuning of multi-layer feed-forward neural network and radial basis function neural network, respectively. In the third and fourth approaches, a back-propagation algorithm has been used along with particle swarm optimization to tune radial basis function neural network. Moreover, in these two approaches, two different clustering algorithms have been utilized to decide the structure of the network. The performances of hybrid approaches (that is, the third and fourth approaches) are found to be better than that of the other two.

Optimization of tile manufacturing process using particle swarm optimization

Abstract: In this paper, an integrated optimization approach using an artificial neural network and a bidirectional particle swarm is proposed. The artificial neural network is used to obtain the relationships between decision variables and the performance measures of interest, while the bidirectional particle swarm is used to perform the optimization with multiple objectives. Finally, the proposed approach is used to solve a process parameter design problem in cement roof-tile manufacturing. The results showed that the bidirectional particle swarm is an effective method for solving multi-objective optimization problems, and that an integrated approach using an artificial neural network and a bidirectional particle swarm can be used to solve complex process parameter design problems.

Sentence selection for generic document summarization using an adaptive differential evolution algorithm

Abstract: For effective multi-document summarization, it is important to reduce redundant information in the summaries and extract sentences, which are common to given documents. This paper presents a document summarization model which extracts key sentences from given documents while reducing redundant information in the summaries. An innovative aspect of our model lies in its ability to remove redundancy while selecting representative sentences. The model is represented as a discrete optimization problem. To solve the discrete optimization problem in this study an adaptive DE algorithm is created. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is to be preferred over summarization systems. We also showed that the resulting summarization system based on the proposed optimization approach is competitive on the DUC2002 and DUC2004 datasets.

Face recognition using bacterial foraging strategy

Abstract: This article presents an efficient algorithm for LDA-based face recognition with the selection of optimal principal components using E-coli Bacterial Foraging Optimization Technique. Different methods were suggested in the literature to select the largest eigenvalues and their corresponding eigenvectors for linear discriminant analysis (LDA). Some researchers have suggested eliminating the three largest eigenvalues to avoid the effect under varying illumination conditions. But, there is no unified approach for selecting optimal eigenvalues to enhance the performance of an algorithm. In this context, a GA–PCA algorithm has been proposed to select optimal eigenvalues and their corresponding eigenvectors in LDA. They proposed a fitness function to find the optimal eigenvectors using the Genetic Algorithm (GA). However, the crossover method used results in differences in offspring, and mutation never allowed them for a physical dispersal of the child in a chosen area. This prevents us in selecting optimal eigenvectors for improvising accuracy of the face recognition algorithm. This has motivated the authors to develop a new algorithm called BFO-Fisher which uses a nutrient concentration function (cost function) for optimization. In this work, the cost function is maximized through hill climbing via a type of biased random walk which is not possible in GA. Here the proposed BFO-Fisher algorithm offers us two distinct additional advantages—(i) the proposed algorithm can supplement the features of GA, and (ii) the random bias incorporated into the BFO algorithm guides us to move in the direction of increasingly favorable environment, which is desirable. In this experiment, both Yale and UMIST Databases are used for the performance evaluation. Experimental results presented in this article reveal the fact that about 3% (Rank 1) improvement can be achieved as compared to the GA-Fisher algorithm.

Model order formulation of a multivariable discrete system using a modified particle swarm optimization approach

Abstract: This paper proposes an algorithm for model order formulation of an absolutely stable higher order linear time invariant multivariable discrete system using a new version of evolutionary computing technique namely, Modified Particle Swarm Optimization (MPSO). A simple adjunct polynomial method has been proposed for obtaining the initial seed values of the lower order multivariable system. In the modified PSO, the movement of a particle is governed by three behaviors namely, inertia, cognitive and social. The cognitive behavior helps the particle to remember its previously visited best position. This paper proposes to split the cognitive behavior into two sections. This modification is efficiently utilized to obtain a better lower order system that reflects the characteristics of the original higher order system by minimizing the integral squared error with the steady state constraints. The results obtained are compared with the earlier techniques utilized, to validate its ease of computation. The proposed algorithm is illustrated with a numerical example from the literature.

A critical assessment of metaheuristics for scheduling emergency infrastructure inspections

Abstract: Search and rescue operations following natural disasters have become the cornerstone for minimizing the adverse social effects and the impact of these hazards. Despite their importance, the literature has not adequately dealt with post disaster operations–at least in part–because of the difficulty in rapidly solving the mathematically complex problems involved. In this paper we consider two important issues within the scope of post natural disaster actions; first, we develop deterministic and probabilistic districting and routing problems for scheduling infrastructure inspection crews following a natural disaster in urban areas; second, we assess and compare five metaheuristic optimization algorithms for solving these districting and routing problems. Results suggest that the five approaches examined offer applicable as well as fast solutions but with varying qualitative characteristics; selection of the preferred approach for practical applications will largely depend upon the network’s characteristics.

Biogeography based optimization for optimal meter placement for security constrained state estimation

Abstract: The paper presents a biogeography based optimization algorithm to design an optimal meter placement scheme which makes the power system network observable. The procedure consists of initially determining the optimal meter set under normal conditions, followed by the optimal reliable meter locations that are obtained under two types of contingencies termed as single meter failure or loss and single branch outages. This is achieved by modifying the derived scheme from the normal condition using penalty functions. The effectiveness of the meter placement algorithm is demonstrated for IEEE standard systems.