Mohamed Abdel-Baset

A hybrid flower pollination algorithm for solving ill-conditioned set of equations

In this paper, we propose a novel technique to solve the ill-conditioned system of linear and nonlinear equations. The aim of hybridisation is to combine the feature of flower pollination algorithm FPA and conjugate direction CD method. Flower pollination algorithm is employed for fast convergence and finds more than one root as well as CD method is used to increase the accuracy of the final results and avoids falling into local minima. Conjugate direction flower pollination algorithm CDFPA will be used to solve system of linear equations. The proposed algorithm retained the global search capability with more accurate and faster convergence. Numerical simulation results of all tested problems show that the proposed algorithm proved to be superior in convergence efficiency, and computational accuracy.

A Complex-Valued Encoding Satin Bowerbird Optimization Algorithm for Global Optimization

The real-valued satin bowerbird optimization (SBO) is a novel bio-inspired algorithm which imitates the ‘male-attracts-the-female for breeding’ principle of the specialized stick structure mechanism of satin birds. SBO has achieved success in congestion management, accurate software development effort estimation. In this paper, a complex-valued encoding satin bowerbird optimization algorithm (CSBO) is proposed aiming to enhance the global exploration ability. The idea of complex-valued coding and finds the optimal one by updating the real and imaginary parts value. With Complex-valued coding increase the diversity of the population, and enhance the global exploration ability of the basic SBO algorithm. The proposed CSBO optimization algorithm is compared against SBO and other state-of-art optimization algorithms using 20 benchmark functions. Simulation results show that the proposed CSBO can significantly improve the convergence accuracy and convergence speed of the original algorithm.

An Improved Most Valuable Player Algorithm with Twice Training Mechanism

The most valuable player algorithm is inspired from these players who want to win the Most Valuable Player (MVP) trophy, it have higher overall success percentage. Teaching-learning-based optimization (TLBO) simulates the process of teaching and learning. TLBO has fewer parameters that must be determined during the renewal process. This paper proposes twice training mechanism to enhance the search ability of the most valuable player algorithm (MVPA) through hybrid TLBO algorithm, and named it teaching the most valuable player algorithm (TMVPA). In TMVPA, designs two behaviors of training and abstract two training modes: pre-competition training and post-competition training. Before individual competition, join the pre-competition training to coordinated exploitation ability and the exploration ability of the original algorithm and join the post-competition training to prevent from falling into the local optimal field after the corporate competition. We test three benchmark functions and an engineering design problem. Results show that TMVPA has effectively raised algorithm accuracy.

Stellar population analysis of galaxies based on improved flower pollination algorithm

When numerical simulations are used for determining the age and contribution of different stellar populations in the integrated colour of a galaxy some problems were encountered. In this paper, a modified flower pollination algorithm (MFPA) is proposed for determining the age and relative contribution of different stellar populations of galaxies. The results show that the proposed algorithm can search efficiently through the very large space of the possible ages for the different integrated colour of galaxies. The proposed algorithm will be applied to an integrated colour of galaxy NGC 3384. The numerical results and statistical analysis show that the proposed algorithm performs significantly better than a previously used genetic algorithm (Attia et al., 2005) and cuckoo search (Abdel-Baset et al., 2015). The study revealed that the proposed algorithm can successfully be applied to a wide range of stellar population and space optimisation problems.

Solving systems of nonlinear equations via conjugate direction flower pollination algorithm

In this paper, we propose a new hybrid algorithm for solving system of nonlinear equations. The aim of hybridisation is to utilise the feature of flower pollination algorithm (FPA) and conjugate direction method (CD). Conjugate direction flower pollination algorithm (CDFPA) combines the advantages of CD and FPA. The problem of solving nonlinear equations is equivalently changed to the problem of function optimisation and then a solution is obtained by CDFPA. The results show the proposed algorithm has high convergence speed and accuracy for solving nonlinear equations.