Robert G. Reynolds

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

In recent years, Cultural Algorithms (CAs) have attracted substantial research interest. When applied to highly multimodal and high dimensional problems, Cultural Algorithms suffer from fast convergence followed by stagnation. This research proposes a novel hybridization between Cultural Algorithms and a modified multiple trajectory search (MTS). In this hybridization, a modified version of Cultural Algorithms is applied to generate solutions using three knowledge sources namely situational knowledge, normative knowledge, and topographic knowledge. From these solutions, several are selected to be used by the modified multi-trajectory search. All solutions generated by both component algorithms are used to update the three knowledge sources in the belief space of Cultural Algorithms. In addition, an adaptive quality function is used to control the number of function evaluations assigned to each component algorithm according to their success rates in the recent past iterations. The function evaluations assigned to Cultural Algorithms are also divided among the three knowledge sources according to their success rates in recent generations of the search. Moreover, the quality function is used to tune the number of offspring these component algorithms are allowed to contribute during the search. The proposed hybridization between Cultural Algorithms and the modified trajectory-based search is employed to solve a test suite of 25 large-scale benchmark functions. The paper also investigates the application of the new algorithm to a set of real-life problems. Comparative studies show that the proposed algorithm can have superior performance on more complex higher dimensional multimodal optimization problems when compared with several other hybrid and single population optimizers.

A modified cultural algorithm with a balanced performance for the differential evolution frameworks

Numerous different methodologies have been introduced in the last few decades to provide efficient solutions for complex real-world problems and other optimization problems. This work focuses on the development of a simple hybrid cultural learning theme with a balanced performance for differential evolution frameworks. It is intended to be always efficient for a diverse set of optimization tasks. As different optimization algorithms behave differently depending on the problems, the combination of the best behaviors from different search strategies seems desirable. The proposed work explores the combination of the explorative/exploitative strengths of two heuristic search techniques, which discretely provide competitive results. Differential evolution is used as the population space for Cultural Algorithm, and is used to guide knowledge dissemination from the knowledge sources in the belief space. Here, a new influence function is introduced that adjusts the membership of each of the knowledge sources. The algorithm has been tested with the conditions and benchmark problems defined for the IEEE CEC2013 special session and competition on real-parameter single objective optimization. The paper also investigates the application of the new algorithm to a set of real-life problems concerning optimizing the weight a tension/compression spring and minimizing the fabrication cost of a welded beam engineering problem. The proposed algorithm appears to have a significant impact on the algorithmic functioning as it reliably augments the performance of the differential evolution frameworks with which it is integrated. Benchmark results for most of the synthetic functions from the special session show that the balanced hybrid obtains superior performance compared to the other competent algorithms. It scales well with the increasing dimensionality and converges in the close proximity of the global optimum for complex functions.

Using agent-based modeling and cultural algorithms to predict the location of submerged ancient occupational sites

Some of the most pivotal questions in human history, such as the origins of early human culture, the spread of hominids out of Africa, and the colonization of New World necessitate the investigation of archaeological sites that are now under water. These contexts have unique potentials for preserving ancient sites without disturbance from later human occupation. The Alpena- Amberley Ridge (AAR) beneath modern Lake Huron in the North American Great Lakes offers unique evidence of prehistoric caribou hunters for a time period that is very poorly known on land.