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

A study of the two unit certainty neuron fuzzy cognitive map

This paper describes a systolic architecture implementing an artificial neural network algorithm for the solution of the problem of detecting a subset of elements of a set, which elements have a given property.

Systolic implementation of neural networks for searching sets of properties

This work presents scalable algorithms for basic construction of parallel Radial Basis Probabilistic Neural Networks. The final goal is to build a neural network that can efficiently be implemented in distributed memory machines. Thus a fast simple parallel training scheme for RBPNNs is studied, that is based almost solely on Gaussian summations which can by their part be efficiently mapped on parallel as well as on pipeline distributed machines. The suggested training scheme is tested for accuracy and performance and can guarantee simplicity, parallelization and linear speed ups in common parallel implementations, namely neuron parallel and pipelining studied here.

A Parallel Radial Basis Probabilistic Neural Network for Scalable Data Mining in Distributed Memory Machines

Processor array architectures for flexible approximate string matching

Multiple pattern matching is widely used in computational biology to locate any number of nucleotides in genome databases. Processing data of this size often requires more computing power than a sequential computer can provide. A viable and cost-effective solution that can offer the power required by computationally intensive applications at low cost is to share computational tasks among the processing nodes of a high performance hybrid distributed and shared memory platform that consists of cluster workstations and multi-core processors. This paper presents experimental results and a theoretical performance model of the hybrid implementations of the Commentz-Walter, Wu-Manber, Set Backward Oracle Matching and the Salmela-TarhioKytojoki family of multiple pattern matching algorithms when executed in parallel on biological sequence databases.

Konstantinos G. Margaritis

Papers

A study of the two unit certainty neuron fuzzy cognitive map

Systolic implementation of neural networks for searching sets of properties

A Parallel Radial Basis Probabilistic Neural Network for Scalable Data Mining in Distributed Memory Machines

Processor array architectures for flexible approximate string matching

Performance study of parallel hybrid multiple pattern matching algorithms for biological sequences