Modern optimization approaches have attracted many research scientists, decision makers and practicing researchers in recent years as powerful intelligent computational techniques for solving several complex real-world problems. The Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics highlights the latest research innovations and applications of algorithms designed for optimization applications within the fields of engineering, IT, and economics. Focusing on a variety of methods and systems as well as practical examples, this book is a significant resource for graduate-level students, decision makers, and researchers in both public and private sectors who are seeking research-based methods for modeling uncertain real-world problems..

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics

Currently, there is a remarkable focus on green technologies for taking steps towards more use of renewable energy sources within the sector of transportation and also decreasing pollution. At this point, employment of plug-in hybrid electric vehicles (PHEVs) needs sufficient charging allocation strategy, by running smart charging infrastructures and smart grid systems. In order to daily usage of PHEVs, daytime charging stations are required and at this point, only an appropriate charging control and a management of the infrastructure can lead to wider employment of PHEVs. In this study, four swarm intelligence based optimization techniques: particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization, and hybrid version of PSO and GSA (PSOGSA) have been applied for the state-of-charge optimization of PHEVs. In this research, hybrid PSOGSA has performed very well in producing better results than other stand-alone optimization techniques.

Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle

Speech enhancement is used in almost all the modern communication systems. It is obvious that when speech is being transmitted, its quality may degrade due to interference in the environment it is passing through. Some of the interferences that may affect the speech quality of transit include acoustic additive noise, acoustic reverberation or white Gaussian noise. This paper focuses on the techniques that appeared in the literature to enhance the signal of speech. Various methods used include wiener filter, statistical methods, subspace method, basic spectral subtraction method and spectral subtraction. In this paper authors will discuss various such methods along with their advantages and disadvantages. The discussion will also review the studies conducted by other researchers on other machine learning techniques, such as Neural network, Deep Neural Network ,Convolution Neural Networks and optimization techniques which used for the enhancement of speech.

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A Survey on Techniques for Enhancing Speech

Green technologies gain popularity to reduce the pollution and give higher penetration of renewable energy source in the transportation. This research induce that the extensive involvement of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. It is also noticed that daytime charging station are necessary for daily usage of PHEVs due to the limited all-electric-range. Most of the researches in the past have been stated that only proper charging control and infrastructure management can assure the larger participation of PHEVs. Therefore, researchers are trying to develop efficient control mechanism for charging infrastructure in order to facilitate upcoming PHEVs penetration in highway. Nevertheless, most of the past researcher already aware with the issue related to intelligent energy management. Yet, these studies could not fill the gap of the problem associated with intelligent ener...

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

The deployment of wireless mesh routers is a crucial task for improving network performance. Therefore, it should be taken seriously to ensure the network accessibility in terms of coverage and connectivity. This placement problem of mesh routers in wireless mesh networks represents an instance of multi-objective optimization problems with huge searching space to explore. In the literature, various optimization algorithms have been applied to find a trade-off between client coverage and network connectivity. To find an optimal mesh router placement, in this paper, we consider Accelerated particle swarm optimizer (APSO) due to its rapid convergence and low computational complexity compared to other population-based algorithms. We have experimentally evaluated it using different generated benchmarks of multiple configurations. The experimental results show that APSO algorithm provides very promising results compared to linearly decreasing weight particle swarm optimizer (LDWPSO).

Accelerated PSO algorithm applied to clients coverage and routers connectivity in wireless mesh networks

A new approach to dual channel speech enhancement is proposed based on a recently introduced meta-heuristic optimization algorithm called hybrid PSOGSA. It is a novel algorithm which combines the ability of exploration in gravitational search algorithm (GSA) and the exploitation capability of particle swarm optimization (PSO) to offer a better local search process along with the social thinking. This paper aims to present such a hybrid combination as a promising and powerful technique to adaptive noise cancellation in speech enhancement and it is compared with the standard PSO (SPSO) and GSA based speech enhancement algorithms. Simulation results prove that the performance of PSOGSA is superior to SPSO and GSA algorithms, in the context of speech enhancement.

A new approach to dual channel speech enhancement based on hybrid PSOGSA

This book serves as a basic reference for those interested in the application of metaheuristics to speech enhancement. The major goal of the book is to explain the basic concepts of optimization methods and their use in heuristic optimization in speech enhancement to scientists, practicing engineers, and academic researchers in speech processing. The authors discuss why it has been a challenging problem for researchers to develop new enhancement algorithms that aid in the quality and intelligibility of degraded speech. They present powerful optimization methods to speech enhancement that can help to solve the noise reduction problems. Readers will be able to understand the fundamentals of speech processing as well as the optimization techniques, how the speech enhancement algorithms are implemented by utilizing optimization methods, and will be given the tools to develop new algorithms. The authors also provide a comprehensive literature survey regarding the topic.

Metaheuristic Applications to Speech Enhancement

This chapter deals with the fundamentals of the optimization. The concepts of stochastic optimization and how the stochastic optimization is advantageous over the deterministic approaches are described in Sect. 3.2. Heuristic and meta-heuristic optimization techniques are defined in Sect. 3.3, and it also presents various existing heuristic and meta-heuristic optimization techniques. The fundamentals of the swarm intelligence are given in Sect. 3.4. The applications of the swarm intelligence in various fields are also presented in this section.

Heuristic and Meta-Heuristic Optimization

Bat Algorithm (BA) is a recently developed population-based meta-heuristic approach (Yang 2010) which is inspired by the hunting behaviour of the bats. BA is developed by Yang (2010). As a novel feature BA is based on the echolocation behaviour of microbats. BA uses the frequency tuning technique to increase the diversity of the solutions in the population, while at the same time it uses the automatic zooming and tries to balance the exploration and exploitation during the search process by mimicking the pulse emission rate and loudness of bats when searching for a prey. This algorithm turns out to be very efficient for a diverse range of problems, and its binary version has been successfully applied to image processing and classifications. The autozooming ability in microbats is manifested in the BA as automatic adjustment from exploration to exploitation when the global optimality is approaching. This is the first algorithm of its kind in terms of balancing these two key components. As a result it proves to be a very efficient optimization technique than all other meta-heuristic algorithms. This chapter intends to present the BA as an improved approach to adaptive noise cancellation in speech enhancement. Simulation results with BA are compared with the standard PSO (Kennedy et al. 2005), accelerated PSO (Yang 2010), gravitational search algorithm (Rashedi et al. 2009) and hybrid PSOGSA (Mirjalili et al. 2010)-based speech enhancement algorithms that are dealt with in this thesis. Conclusions of the present chapter clearly establish the advantages of the new meta-heuristic BA over the other algorithms in the context of speech enhancement.

Speech Enhancement Based on Bat Algorithm (BA)

The research methodology involved in the implementation of ANC via heuristic algorithm and the different stages involved in the implementation process are described in Sect. 4.1. The selection of the objective function for adaptive noise cancellation is described in Sect. 4.2. This chapter discusses the previously proposed meta-heuristic algorithms to ANC in dual-channel speech enhancement. All the previously proposed methods are presented in Sect. 4.3. The mathematical formulae and the background concepts of five variants of PSO algorithms called modified PSO, learning-based PSO, hybrid PSO, improved PSO, and asexually reproduced adaptive quantum PSO algorithms are presented in this section. Finally, the conclusion of this chapter is presented in Sect. 4.4.

Prajna Kunche

Papers

A new approach to dual channel speech enhancement based on hybrid PSOGSA

Metaheuristic Applications to Speech Enhancement

Heuristic and Meta-Heuristic Optimization

Speech Enhancement Based on Bat Algorithm (BA)

Application of Meta-Heuristics to Speech Enhancement