Swagatam Das

A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking

Abstract Due to its simplicity and efficiency, a recently proposed optimization algorithm, Sine Cosine Algorithm (SCA), has gained the interest of researchers from various fields for solving optimization problems. However, it is prone to premature convergence at local minima as it lacks internal memory. To overcome this drawback, a novel Hybrid SCA-PSO algorithm for solving optimization problems and object tracking is proposed. The P b e s t and G b e s t components of PSO (Particle Swarm Optimization) is added to traditional SCA to guide the search process for potential candidate solutions and PSO is then initialized with P b e s t of SCA to exploit the search space further. The proposed algorithm combines the exploitation capability of PSO and exploration capability of SCA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 23 classical, CEC 2005 and CEC 2014 benchmark functions. Statistical parameters are employed to observe the efficiency of the Hybrid SCA-PSO qualitatively and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The Hybrid SCA-PSO algorithm is applied for object tracking as a real thought-provoking case study. Experimental results show that the Hybrid SCA-PSO-based tracker can robustly track an arbitrary target in various challenging conditions. To reveal the capability of the proposed algorithm, comparative studies of tracking accuracy and speed of the Hybrid SCA-PSO based tracking framework and other trackers, viz., Particle filter, Mean-shift, Particle swarm optimization, Bat algorithm, Sine Cosine Algorithm (SCA) and Hybrid Gravitational Search Algorithm (HGSA) is presented.

Metaheuristic Approach to PSP—An Overview of the Existing State-of-the-art

This chapter provides an overview of the research in protein structure prediction with metaheuristic techniques using AB off-lattice model. The chapter discuses related work covered under the heads of metaheuristic methods, classified into four categories. The scope of work has been outlined and finally, the chapter ends with a discussion of the contributions we have made in this book.

Backgrounds on Protein Structure Prediction and Metaheuristics

This chapter provides a comprehensive overview of the protein structure prediction problem based on metaheuristic algorithms. At first, the basic concepts of proteins, the level of protein structure have been presented in a formal way. A computational model, as well as techniques, have been addressed for solving protein structure prediction (PSP) problem. The chapter discusses the basic fundamentals of metaheuristics algorithms in detail and finally ends with a discussion of techniques are used in the book towards solving the problem.

Protein Structure Prediction Using Improved Variants of Metaheuristic Algorithms

This chapter introduces four schemes for protein structure prediction (PSP) based on 2D and 3D AB off-lattice model. The proposed methods are based on the modified versions of the classical PSO, BA, BBO and HS algorithms, providing an improved solution using different strategies. The strategies are developed in order to find global minimum energy value over the multi-modal landscape structure of the PSP problem. The performance of the proposed methods are extensively compared with the algorithms which are applied to the PSP problem over a test suite of several artificial and real-life protein instances.

The Lévy Distributed Parameter Adaptive Differential Evolution for Protein Structure Prediction

This chapter introduces a scheme for controlling parameters adaptively in the differential evolution (DE) algorithm. In the proposed method, parameters of DE are adapted using Levy distribution. The distribution function allows possible changes with a significant amount in the control parameters adaptively, which provides good exploration and exploitation in the search space to reach the global optimum point. The performance of the Levy distributed DE algorithm has been extensively studied and compared with some parameter control techniques over a test-suite of unimodal, basic and expanded multi-modal and hybrid composite functions with different dimensions. The proposed method is also investigated on real protein sequences for protein structure prediction. The results exhibits that the Levy distributed DE algorithm provides significant performance in terms of accuracy and convergence speed to obtain global optimum solution.

Continuous Landscape Analysis Using Random Walk Algorithm

This chapter describes a chaos based random walk (CRW) algorithm for analyzing landscape structure in continuous search spaces. Unlike the existing random walks, no fixed step size is required in the proposed algorithm, rather conduct the random walk. The chaotic map is used to generate the chaotic pseudo random numbers (CPRN) for determining the variable-scaled step size and direction. The superiority of the new method has been demonstrated while comparing it with the simple and progressive random walk algorithms using histogram analysis. The performance of the proposed CRW algorithm is evaluated on the IEEE Congers on Evolutionary Computation (CEC) 2013 benchmark functions in continuous search space having different levels of complexity. The proposed method is applied to analyzing the landscape structure for protein structure prediction problem in continuous search space.

Hybrid Metaheuristic Approach for Protein Structure Prediction

Hybridization is an integrated framework that combines the merits of algorithms to improve the performance of an optimizer. In this chapter, the synergism of the improved version of particle swarm optimization (PSO) and differential evolution (DE) algorithms are invoked to construct a hybrid algorithm. The proposed method is executed in an interleaved fashion for balancing exploration and exploitation dilemma in the evolution process. The results are tested on ten real protein instances, taken from the protein data bank. The effectiveness of the proposed algorithm is evaluated through qualitative and quantitative comparisons with other hybridization of PSO and DE; and comprehensive learning PSO algorithms.

Landscape Characterization and Algorithms Selection for the PSP Problem

Fitness landscape analysis (FLA) is a technique to determine the characteristics of a problem or its structural features based on which the most appropriate algorithm is possible to recommend for solving the problem. In this chapter, we determine structural features of the protein structure prediction problem by analyzing the landscape structure. A landscape of the protein instances is generated by using the quasi-random sampling technique and city block distance. Structural features of the PSP Landscape are determined by applying various landscape measures. Numerical results indicate that the complexity of the PSP problem increases with protein sequence length. Six well-known real-coded optimization algorithms are evaluated over the same set of protein sequences and the performances are subsequently analyzed based on the structural features. Finally, we suggest the most appropriate algorithm(s) for solving the PSP problem.

Protein Structure Optimization in 3D AB off-lattice model using Biogeography Based Optimization with Chaotic Mutation

Protein structure prediction (PSP) from its amino acid sequence is a challenging problem in computational biology and can be considered as a global optimization problem. It is a multi-modal optimization problem and belongs to NP-hard class. In this paper, Biogeography Based Optimization with Chaotic Mutation (BBO-CM) algorithm has been developed to optimize 3D protein structure. The proposed algorithm prevents premature convergence and jumping out from the local minima during execution and converges with the optimum solution. Chaos system generates the chaotic pseudo random sequence which is utilized in mutation operation of BBO algorithm to increase the population diversity. The experiments are carried out with artificial and real protein sequences with different length to confirm the performance and robustness of the BBO-CM algorithm. Results are compared with other algorithms demonstrating the efficiency of the proposed approach.