Abstract
Developing metaheuristic algorithms has been paid more recent attention from researchers and scholars to address the optimization problems in many fields of studies. This paper proposes a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm for global optimization. Enhanced population diversity for adaptation multi-group quasi-affine transformation evolutionary algorithm is implemented by randomly dividing its population into three groups. Each group adopts a mutation strategy differently for improving the efficiency of the algorithm. The scale factor F of mutations is updated adaptively during the search process with the different policies along with proper parameter to make a better trade-off between exploration and exploitation capability. In the experimental section, the CEC2013 test suite and the node localization in wireless sensor networks were used to verify the performance of the proposed algorithm. The experimental results are compared results with three quasi-affine transformation evolutionary algorithm variants, two different evolution variants, and two particle swarm optimization variants show that the proposed adaptation multi-group quasi-affine transformation evolutionary algorithm outperforms the competition algorithms. Moreover, analyzed results of the applied adaptation multi-group quasi-affine transformation evolutionary for node localization in wireless sensor networks showed that the proposed method produces higher localization accuracy than the other competing algorithms.
Highlights
Over the last few decades, global optimization problems have attracted a lot of research interest [1].Many optimization algorithms have been developed based on inspiration from natural phenomenon, e.g., biological, swarm, physical aspects that are known as natural-inspired intelligent algorithms [2].The natural-inspired smart algorithms have been widely applied to solve optimization problems successfully [3–5]
In order to reduce above weaknesses, we proposed an improved quasi-affine transformation evolution algorithm (QUATRE), called AMG-QUATRE, which is made up of population initialization, random population division, group evolution, and group recombining and adaption method for updating parameter scale factor F
In order to reduce the estimation error, we present an improved distance vector-hop (DV-Hop) based on AMG-QUATRE for node localization in wireless sensor networks (WSN)
Summary
Over the last few decades, global optimization problems have attracted a lot of research interest [1].Many optimization algorithms have been developed based on inspiration from natural phenomenon, e.g., biological, swarm, physical aspects that are known as natural-inspired intelligent algorithms [2].The natural-inspired smart algorithms have been widely applied to solve optimization problems successfully [3–5]. Over the last few decades, global optimization problems have attracted a lot of research interest [1]. Many optimization algorithms have been developed based on inspiration from natural phenomenon, e.g., biological, swarm, physical aspects that are known as natural-inspired intelligent algorithms [2]. The natural-inspired smart algorithms have been widely applied to solve optimization problems successfully [3–5]. Genetic algorithm (GA) [6], particle swarm optimization (PSO) [7], differential. The natural-inspired algorithms have been proving robust in delivering optimal global solutions and assisting in resolving the limitations encountered in traditional methods [17]. The optimization process of the natural-inspired intelligent algorithms usually begins with generating a set of randomly initialized agents that combined, immigrated, or evolved over a predefined number of generations
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