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

Parallel direct methods for solving the system of linear equations with pipelining on a multicore using OpenMP

This paper proposes a generic programmable array processor architecture for a wide variety of approximate string matching algorithms. Further, we describe the architecture of the array and the architecture of the cell in detail in order to efficiently implement for both the preprocessing and searching phases of most string matching algorithms. Further, the architecture performs approximate string matching for complex patterns that contain don't care, complement and classes symbols. Our architecture maximizes the strength of VLSI in terms of intensive and pipelined computing and yet circumvents the limitation on communication. It may be adopted as a basic structure for a universal flexible string matcher engine.

A programmable array processor architecture for flexible approximate string matching algorithms

Scientific computations on multi-core systems using different programming frameworks

Kernel density estimation is nowadays very popular tool for nonparametric probabilistic density estimation. One of its most important disadvantages is computational complexity of computations needed, especially for large data sets. One way for accelerating these computations is to use the parallel computing with multi-core platforms. In this paper we parallelize two kernel estimation methods such as the univariate and multivariate kernel estimation from the field of the computational econometrics on multi-core platform using different programming frameworks such as Pthreads, OpenMP, Intel Cilk++, Intel TBB, SWARM and FastFlow. The purpose of this paper is to present an extensive quantitative (i.e., performance) and qualitative (i.e., the ease of programming effort) study of the multi-core programming frameworks for these two kernel estimation methods.

Parallel Computing of Kernel Density Estimation with Different Multi-core Programming Models

Collaborative Filtering (CF) is a popular technique employed by Recommender Systems, a term used to describe intelligent methods that generate personalized recommendations. The most common and accurate approaches to CF are based on latent factor models. Latent factor models can tackle two fundamental problems of CF, data sparsity and scalability and have received considerable attention in recent literature. In this work, we present an optimal scaling approach to address both of these problems using Categorical Principal Component Analysis for the low-rank approximation of the user-item ratings matrix, followed by a neighborhood formation step. The optimal scaling approach has the advantage that it can be easily extended to the case when there are missing data and restrictions for ordinal and numerical variables can be easily imposed. We considered different measurement levels for the user ratings on items, starting with a multiple nominal and consecutively applying nominal, ordinal and numeric levels. Experiments were executed on the MovieLens dataset, aiming to evaluate the aforementioned options in terms of accuracy. Results indicated that a combined approach (multiple nominal measurement level, ‘‘passive’’ missing data strategy) clearly outperformed the other tested options.

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Konstantinos G. Margaritis

Papers

Parallel direct methods for solving the system of linear equations with pipelining on a multicore using OpenMP

A programmable array processor architecture for flexible approximate string matching algorithms

Scientific computations on multi-core systems using different programming frameworks

Parallel Computing of Kernel Density Estimation with Different Multi-core Programming Models

An Optimal Scaling Approach to Collaborative Filtering Using Categorical Principal Component Analysis and Neighborhood Formation