Ameliorated ensemble strategy-based evolutionary algorithm with dynamic resources allocations

In the last two decades, evolutionary computing has become the mainstream to attract the attention of the experts in both academia and industrial applications due to the advent of the fast computer with multi-core GHz processors have had a capacity of processing over 100 billion instructions per second. Today’s different evolutionary algorithms are found in the existing literature of evolutionary computing that is mainly belong to swarm intelligence and nature-inspired algorithms. In general, it is quite realistic that not always each developed evolutionary algorithms can perform all kinds of optimization and search problems. Recently, ensemble-based techniques are considered to be a good alternative for dealing with various benchmark functions and real-world problems. In this paper, an ameliorated ensemble strategy-based evolutionary algorithm is developed for solving large-scale global optimization problems. The suggested algorithm employs the particle swam optimization, teaching learning-based optimization, differential evolution, and bat algorithm with a self-adaptive procedure to evolve their randomly generated set of solutions. The performance of the proposed ensemble strategy-based evolutionary algorithm evaluated over thirty benchmark functions that are recently designed for the special session of the 2017 IEEE congress of evolutionary computation (CEC’17). The experimental results provided by the suggested algorithm over most CEC’17 benchmark functions are much promising in terms of proximity and diversity.