Author
Jason G. Digalakis
Bio: Jason G. Digalakis is an academic researcher from University of Macedonia. The author has contributed to research in topics: Crossover & Memetic algorithm. The author has an hindex of 6, co-authored 8 publications receiving 569 citations.
Papers
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TL;DR: Experimental results on the major benchmarking functions used for performance evaluation of Genetic Algorithms (GAs) are presented, including the effect of population size, crossover probability, mutation rate and pseudorandom generator.
Abstract: This paper presents experimental results on the major benchmarking functions used for performance evaluation of Genetic Algorithms (GAs). Parameters considered include the effect of population size, crossover probability, mutation rate and pseudorandom generator. The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.
340 citations
TL;DR: The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State replacement Model (SSRM) is evaluated.
Abstract: This paper presents a review and experimental results on the major benchmarking functions used for performance control of Genetic Algorithms (GAs). Parameters considered include the effect of population size, crossover probability and pseudo-random number generators (PNGs). The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.
197 citations
TL;DR: This paper proposes a method to solve the maintenance scheduling problem, called the parallel co-operating cultural algorithm (PARCA), which combines the weak search method with the knowledge representation scheme for collecting and reasoning knowledge about individual experience.
61 citations
TL;DR: A global parallelization approach is adopted that preserves the properties, behavior, and fundamental of the sequential algorithm.
48 citations
08 Oct 2000
TL;DR: This paper presents a review and experimental results of major benchmarking functions used for the performance control of genetic algorithms (GAs).
Abstract: This paper presents a review and experimental results of major benchmarking functions used for the performance control of genetic algorithms (GAs) Parameters considered include the effect of population size, crossover probability and pseudo-random number generators The general computational behavior of two basic GAs models, the Generational Replacement Model and the Steady State Replacement Model is evaluated
32 citations
Cited by
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TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
10,082 citations
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
7,090 citations
Book•
30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Abstract: 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.
5,994 citations
TL;DR: 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.
Abstract: 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 .
3,088 citations
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TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
3,027 citations