Noor H. Awad

Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization

Differential evolution (DE) is a simple, yet very effective, population-based search technique. However, it is challenging to maintain a balance between exploration and exploitation behaviors of the DE algorithm. In this paper, we boost the population diversity while preserving simplicity by introducing a multi-population DE to solve large-scale global optimization problems. In the proposed algorithm, called mDE-bES, the population is divided into independent subgroups, each with different mutation and update strategies. A novel mutation strategy that uses information from either the best individual or a randomly selected one is used to produce quality solutions to balance exploration and exploitation. Selection of individuals for some of the tested mutation strategies utilizes fitness-based ranks of these individuals. Function evaluations are divided into epochs. At the end of each epoch, individuals between the subgroups are exchanged to facilitate information exchange at a slow pace. The performance of the algorithm is evaluated on a set of 19 large-scale continuous optimization problems. A comparative study is carried out with other state-of-the-art optimization techniques. The results show that mDE-bES has a competitive performance and scalability behavior compared to the contestant algorithms.

An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems

An effective and efficient self-adaptation framework is proposed to improve the performance of the L-SHADE algorithm by providing successful alternative adaptation for the selection of control parameters. The proposed algorithm, namely LSHADE-EpSin, uses a new ensemble sinusoidal approach to automatically adapt the values of the scaling factor of the Differential Evolution algorithm. This ensemble approach consists of a mixture of two sinusoidal formulas: A non-Adaptive Sinusoidal Decreasing Adjustment and an adaptive History-based Sinusoidal Increasing Adjustment. The objective of this sinusoidal ensemble approach is to find an effective balance between the exploitation of the already found best solutions, and the exploration of non-visited regions. A local search method based on Gaussian Walks is used at later generations to increase the exploitation ability of LSHADE-EpSin. The proposed algorithm is tested on the IEEE CEC2014 problems used in the Special Session and Competitions on Real-Parameter Single Objective Optimization of the IEEE CEC2016. The results statistically affirm the efficiency and robustness of the proposed approach to obtain better results compared to L-SHADE algorithm and other state-of-the-art algorithms.

A Differential Evolution algorithm with success-based parameter adaptation for CEC2015 learning-based optimization

Developing efficient evolutionary algorithms for solving learning-based real-parameter single objective optimization is a very challenging and essential task in many real applications. This task involves finding the best optimal solution with least computational cost, avoiding premature convergence. This paper proposes a new efficient Differential Evolution algorithm with success-based parameter adaptation with resizing population space. We introduce a new technique to adapt the control parameters which uses a memory-based structure of previous successful settings. Moreover, the population size is adapted linearly to find the most suitable size which helps to guide the search in each optimization loop. The proposed algorithm is tested on the benchmarks of the CEC2015 real parameter single objective competition. The results affirm the efficiency and robustness of our approach to reach good results.

A novel class of niche hybrid Cultural Algorithms for continuous engineering optimization

This paper proposes a novel class of niche hybrid Cultural Algorithms for solving engineering problems with continuous design variables. The first algorithm, Niche Cultural Algorithm (NCA), embeds niching within the cultural framework to maintain multiple groups within the population of agents in order to locate multiple optima. The second algorithm, hybridizes niche Cultural Algorithms with Tabu search (H-NCA). This technique offers a novel architecture of hybrid approaches, which combines Niche Cultural Algorithms (NCA) with Tabu search (TS). The proposed hybridization scheme enables the algorithm to overleap local optima and improve performance. The third algorithm, Improved Hybrid Niche Cultural Algorithms (IH-NCA), is employed to enhance convergence rate and accuracy of H-NCA with fewer computations. In IH-NCA, the algorithm switches between two selection strategies based on roulette wheel and stochastic tournament selection. This enhances the algorithms ability to further escape stagnation and premature convergence with varying stochastic noise and selection pressure. Simulations were performed over miscellaneous engineering optimization problems that include minimization of constrained functions and structural engineering optimization. A comparative study is carried out with other state-of-the-art optimization techniques. The findings affirm the efficiency and robustness of the new methodologies over the other existing relevant approaches.

CADE: A hybridization of Cultural Algorithm and Differential Evolution for numerical optimization

Abstract Many real-world problems can be formulated as optimization problems. Such problems pose a challenge for researchers in the design of efficient algorithms capable of finding the best solution with the least computational cost. In this paper, a new evolutionary algorithm is proposed that combines the explorative and exploitative capabilities of two evolutionary algorithms, Cultural Algorithm (CA) and Differential Evolution (DE) algorithm. This hybridization follows the HTH (High-level Teamwork Hybrid) nomenclature in which two meta-heuristics are executed in parallel. The new algorithm named as CADE, manages an overall population which is shared between CA and DE simultaneously. Four modified knowledge sources have been used in proposed CA which are: topographical, situational, normative and domain. The role of the used acceptance function in belief space is to select the knowledge of the best individuals to update the current knowledge. A novel quality function is used to determine the participation ratio for both CA and DE, and then a competitive selection takes place in order to select the proportion of function evaluations allocated for each technique. This collaborative synergy emerges between the DE and CA techniques and is shown to improve the quality of solutions, beyond what each of these two algorithms alone. The performance of the algorithm is evaluated on a set of 50 scalable optimization problems taken from two sources. The first set of 35 came from existing benchmark sets available in the literature. The second set came from the 2014 IEEE Single Function optimization competition. The overall results show that CADE has a favorable performance and scalability behaviors when compared to other recent state-of-the-art algorithms. CADEs overall performance ranked at number 1 for each of the two sets of problems. It is suggested that CADEs success across such a broad spectrum of problem types and complexities bodes well for its application to new and novel applications.

An Adaptive Multipopulation Differential Evolution With Dynamic Population Reduction

Developing efficient evolutionary algorithms attracts many researchers due to the existence of optimization problems in numerous real-world applications. A new differential evolution algorithm,

LEACH enhancements for wireless sensor networks based on energy model

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A novel hybrid Cultural Algorithms framework with trajectory-based search for global numerical optimization

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A decremental stochastic fractal differential evolution for global numerical optimization

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Cultural Algorithm with improved local search for optimization problems

, is proposed to improve the search quality, avoid premature convergence, and stagnation. The population is clustered in multiple tribes and utilizes an ensemble of different mutation and crossover strategies. In this algorithm, a competitive success-based scheme is introduced to determine the life cycle of each tribe and its participation ratio for the next generation. In each tribe, a different adaptive scheme is used to control the scaling factor and crossover rate. The mean success of each subgroup is used to calculate the ratio of its participation for the next generation. This guarantees that successful tribes with the best adaptive schemes are only the ones that guide the search toward the optimal solution. The population size is dynamically reduced using a dynamic reduction method. Comprehensive comparison of the proposed heuristic over a challenging set of benchmarks from the CEC2014 real parameter single objective competition against several state-of-the-art algorithms is performed. The results affirm robustness of the proposed approach compared to other state-of-the-art algorithms.