Browsing by Author "Javaid Ali"

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    Artificial showering algorithm: a new meta-heuristic for unconstrained optimization
    (Science International, 2015) Javaid Ali; Muhammad Saeed; Muhammad Luqman; Muhammad Farhan Tabassum
    A novel meta-heuristic known as Artificial Showering Algorithm (ASHA) is presented in this paper. The proposed method is based on flow and accumulation phenomena of water units distributed by human controlled equipment in an ideal field representing the search space. The developed method is applied to benchmarking test functions and quality solutions are obtained. Comparisons witness that the method even at its evolvement phase performs better than pioneering algorithms like Genetic Algorithm, Differential Evolution and Simulated Annealing method.
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    Development of hybrid metaheuristic for global optimization
    (UMT Lahore, 2019-06-13) Javaid Ali
    Metaheuristics is a research area that delivers general purpose high quality optimization algorithms, proved effectual in dealing with complex global optimization problems. Success of metaheuristics greatly depends on their aptitude to establish equilibrium between their essential characters: exploration and exploitation. But the advent of No Free Lunch theorems by Wolpert and Macready established a general opinion that all algorithms perform equally when averaged over the whole function space and hence none of them can be claimed to be the best over the entire function space. For this reason, the basic algorithms require essential refinements and enhancements. The main goal of this thesis is twofold: to develop new effective hybrid metaheuristic strategies for solving selected global optimization problems and to analyze the performances of developed hybrid metaheuristics on mathematical benchmark functions and complex real world problems that can be modeled as global optimization problems. Generally, hybridization is carried out by integrating powerful components of different algorithms. The first hybrid metaheuristic proposed in this work is Controlled Showering Optimization (CSO) algorithm which is a combination of Artificial Showering Algorithm and frame based search mechanism. The second proposed hybrid algorithm is Cooperative Multi-Simplex algorithm (CMSA) that is based on collaborative search of multiple simplexes working under the iterations of a Non- Stagnated Nelder-Mead Simplex algorithm (NS-NMSA). The evolvement of the provably convergent variant NS-NMSA is also carried out in this work by identifying and coping the failures and stagnations of standard Nelder-Mead simplex algorithm. Multi-Simplex Imperialist Competitive Algorithm (MS-ICA) is the third hybrid metaheuristic which is designed by embedding NS-NMSA iterations in Imperialist Competitive Algorithm. The fourth hybrid metaheuristic designed in this continuation is obtained by integrating CMSA and Differential Evolution (DE) algorithm. In a specifically constructed computational framework, this hybrid algorithm in collaboration with Padé approximation is named as hybrid Evolutionary Padé Approximation (EPA) scheme.