فهرس المقالات global optimization

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  1 - Adaptive Rule-Base Influence Function Mechanism for Cultural Algorithm
  Vahid Seydi Ghomsheh Mohamad Teshnehlab Mehdi Aliyari Shoordeli
  This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important t أکثر

  This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule based system is optimized using Genetic Algorithm (GA). The proposed modified CA algorithm is compared with several other optimization algorithms including GA, particle swarm optimization (PSO), especially standard version of cultural algorithm. The obtained results demonstrate that the proposed modification enhances the performance of the CA in terms of global optimality.Optimization is an important issue in different scientific applications. Many researches dedicated to algorithms that can be used to find an optimal solution for different applications. Intelligence optimizations which are generally classified as, evolutionary computations techniques like Genetic Algorithm, evolutionary strategy, and evolutionary programming, and swarm intelligence algorithms like particle swarm intelligence algorithm and ant colony optimization, etc are powerful tools for solving optimization problems تفاصيل المقالة
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  2 - Augmented Downhill Simplex a Modified Heuristic Optimization Method
  Mohsen Jalaeian-F
  Augmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorith أکثر

  Augmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorithm; so, it can be trapped in a local minimum. In contrast, random search is a global exploration, but less efficient. Here, random search is considered as a global exploration operator in combination with DSM as a local exploitation method. Thus, presented algorithm is a derivative-free, fast, simple and nonlinear optimization method that is easy to be implemented numerically. Efficiency and reliability of the presented algorithm are compared with several other optimization methods, namely traditional downhill simplex, random search and steepest descent. Simulations verify the merits of the proposed method. تفاصيل المقالة
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  3 - A Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Global Optimization
  Sosan Sarbazfard Ahmad Jafarian
  In this paper, a new and an eective combination of two metaheuristic algorithms, namely Fire y Algorithm and the Dierential evolution, has been proposed. This hybridization called as HFADE, consists of two phases of Dierential Evolution (DE) and Fire y Algorithm (FA) أکثر

  In this paper, a new and an eective combination of two metaheuristic algorithms, namely Fire y Algorithm and the Dierential evolution, has been proposed. This hybridization called as HFADE, consists of two phases of Dierential Evolution (DE) and Fire y Algorithm (FA). Fire y algorithm is the nature- inspired algorithm which has its roots in the light intensity attraction process of re y in the nature. Dierential evolution is an Evolutionary Algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are eective and powerful algorithms but FA algorithm depends on random directions for search which led into retardation in nding the best solution and DE needs more iteration to nd proper solution. As a result, this proposed method has been designed to cover each algorithm deciencies so as to make them more suitable for optimization in real world domain. To obtain the required results, the experiment on a set of benchmark functions was performed and ndings showed that HFADE is a more preferable and eective method in solving the high-dimensional functions. تفاصيل المقالة