Grey Wolf Optimizer

The Whale Optimization Algorithm

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

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Evolutionary algorithms for solving multi-objective problems

This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html .

SCA: A Sine Cosine Algorithm for solving optimization problems

Salp Swarm Algorithm

In this paper, we describe and compare two distinct algorithms aiming at the low-rank approximation of a user-item ratings matrix in the context of Collaborative Filtering (CF). The first one implements standard Principal Component Analysis (PCA) of an association matrix formed from the original data. The second algorithm is based on h-NLPCA, a nonlinear generalization of standard PCA, which utilizes an autoassociative network, and constrains the nonlinear components to have the same hierarchical order as the linear components in standard PCA. We examine the impact of the aforementioned approaches on the quality of the generated predictions through a series of experiments. Experimental results show that the latter approach outperforms the standard PCA approach for most values of the retained dimensions.

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Collaborative filtering through SVD-based and hierarchical nonlinear PCA

In this paper we present the integration of state of the art, open source, production level tools into a parallel programming environment, used for teaching parallel programming in MPI and OpenMP. Based on experiences introduced during this case study, and opinions gathered by an online questionnaire, a refined set of missing features that would strengthen the environment's educational role is defined.

Towards an Integrated Teaching Environment for Parallel Programming

This paper discusses the development a text-dependent speaker identification system. In particular, we develop a neural network, collect the input corpus, train, and measure its efficiency. The development is done with the CSLU-NN environment, a set of programs in Tcl and C, part of the OGI toolkit, originally built for speech recognition tasks, and adapted to our speaker recognition needs. Thus we propose a well-described and quite simple way for building pattern recognizers that deal with .wav files, in general. Our results demonstrate the efficiency of our implementation.

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Development of a Text-Dependent Speaker Identification System with the OGI Toolkit

This paper describes systolic designs for the Power Method, Matrix Squaring Methods and Raised Matrix Power (RMP) Method, which is a combination of the two first methods, for the calculation of the dominant eigenvalue and the corresponding eigenvector of a matrix. The methods described are appropriate for non-sparse matrices with dominant eigenvalue. The designs were simulated and tested soft systolically in OCCAM.

Systolic designs for eigenvalue-eigenvector computations using matrix powers

A longest common subsequence (LCS) of two strings is a common subsequence of two strings of maximal length. The LCS problem is to find an LCS of two given strings and the length of the LCS (LLCS). In this paper, we present a new linear processor array for solving the LCS problem. The array is based on parallelization of a recent LCS algorithm which consists of two phases, i.e. preprocessing and computation. The computation phase is based on bit-level dynamic programming approach. Implementations of the preprocessing and computation phases are discussed on the same processor array architecture for the LCS problem. Further, we propose a block processor array architecture which reduces the overall communication and time requirements. Finally, we develop a performance model for estimating the performance of the processor array architecture on Pentium processors.

Konstantinos G. Margaritis

Papers

Collaborative filtering through SVD-based and hierarchical nonlinear PCA

Towards an Integrated Teaching Environment for Parallel Programming

Development of a Text-Dependent Speaker Identification System with the OGI Toolkit

Systolic designs for eigenvalue-eigenvector computations using matrix powers

New Processor Array Architectures for the Longest Common Subsequence Problem