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

This paper discusses the efficient implementation of iterative matrix operations on application specific processor arrays. Recursive equations, frequently met in a wide range of problems, can be solved through a new systolic structure, the Reconfigurable Systolic Torus, in almost optimal Area and Time requirements. Moreover, flexibility is maintained with no demand of complex instruction sets or extensive control units implemented inside the systolic cells. The general design together with the computation schedules are presented and the description of this new structure is gradually built towards the satisfaction of some basic predefined criteria.

A reconfigurable systolic torus for iterative matrix operations

This paper is focused on designing two parallel dot product imple- mentations for heterogeneous master-worker platforms. These implementations are based on the data allocation and dynamic load balancing strategies. The first implementation is the dynamic master - worker with allocation of vectors where the master distributes vectors (data) and computations to the workers whereas the second one is the dynamic master - worker with allocation of vector pointers where the vectors are supposed to be replicated among participating resources beforehand and the master distributes computations to the workers. We also report a set of numerical experiments on a heterogeneous platform where computational resources have different computing powers. Also, the workers are connected to the master by links of same capacities. The obtained experimental results demonstrate that the dynamic allocation of vector point- ers achieve better performance than the original one for computing dot product computation. The paper also presents and verifies an accurate timing model to predict the performance of the proposed implementations on clusters of heterogeneous workstations. Through this model the viability of the proposed implementa-

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Computing dot -- product on heterogeneous master -- worker platforms

To solve real-world discrete optimization problems approximately metaheuristics such as memetic algorithms and other evolutionary and local search methods are commonly used. For large instances of these problems or those with a lot of hard constraints even fast heuristics require a considerable amount of computational time. We present PARA.MENOAS, an object - oriented memetic algorithms library based on C++ and using the MPI message passing interface. It provides an automatic, transparent way of parallelizing memetic algorithms. The efficient communication in PARAMENOAS is the main reason for its success in several real - world applications.

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A parallel memetic library for optimization problems

Finite difference and iterative methods are used to simulate two-dimensional impulse propagation in cardiac tissue. The model used takes explicitly into account the effects of cytoplasmic and junctional resistances. An initial parallel approach for the implementation of the iterative methods (Gauss-Seidel in our case) based on the systolic array method is presented. The number of iterations needed for convergence of the iterative methods at each time step is examined in conjunction with changes in the spatial distribution of the electrophysiologic parameters such as coupling resistance. Electrograms and propagating action potentials (PAP) are calculated for cases simulating infarcted hearts by placing dead cell bariers in the model cardiac tissue sheet. The shapes of the electrograms and PAPs are critically depending on the position of the recording site relative to the dead cell barriers.

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Large Scale Modelling Of Two-dimensional Cardiac Tissue Using Parallel Methods

This paper presents an analytical performance prediction model that can be used to predict the execution time, speedup and similar performance metrics of four approximate string searching implementations running on an MPI cluster of heterogeneous workstations. The four implementations are based on master-worker model using static and dynamic allocation of the text collection. The developed performance model has been validated on an 8-cluster of heterogeneous workstations and it has been shown that the model is able to predict the execution time and other performance metrics of four parallel implementations accurately.

Konstantinos G. Margaritis

Papers

A reconfigurable systolic torus for iterative matrix operations

Computing dot -- product on heterogeneous master -- worker platforms

A parallel memetic library for optimization problems

Large Scale Modelling Of Two-dimensional Cardiac Tissue Using Parallel Methods

Performance analysis of approximate string searching implementations for heterogeneous computing platform