Abstract The field of neuroevolution has achieved much attention in recent years from both academia and industry. Numerous papers have reported its successful applications in different fields ranging from medical domain to autonomous systems. However, it is not clear which evolutionary optimization techniques lead to the best results. In this paper, multilayer perceptron (MLP) neural networks (NNs) are trained and optimized using four advanced bio-inspired evolutionary algorithms (EA). The algorithms are Multi-Verse Optimizer (MVO), Moth-flame optimization (MFO), Cuckoo Search (CS) and Particle Swarm Optimization (PSO). Each algorithm is equipped with two operators: evolutionary population dynamics and mutation, which impact on exploration and exploitation. Optimized MLPs are then used for the navigation of an autonomous robot. Accuracy and area under the curve metrics are used for the evaluation and comparison metrics. Moreover, two well-regarded gradient descent algorithms including Back propagation (BP) and Levenberg Marquardt (LM) are utilized to validate the results obtained by evolutionary-based MLP trainers. It is observed that MLPs developed using MFO are the most robust ones among MLPs trained using other evolutionary and gradient descent algorithms.

中文翻译：

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基于神经进化的自主机器人导航：一项比较研究

摘要 近年来，神经进化领域受到了学术界和工业界的广泛关注。许多论文报道了它在从医学领域到自治系统等不同领域的成功应用。但是，尚不清楚哪种进化优化技术会带来最佳结果。在本文中，多层感知器 (MLP) 神经网络 (NN) 使用四种先进的仿生进化算法 (EA) 进行训练和优化。这些算法是 Multi-Verse Optimizer (MVO)、Moth-flame optimization (MFO)、Cuckoo Search (CS) 和 Particle Swarm Optimization (PSO)。每个算法都配备了两个算子：进化种群动态和变异，影响探索和开发。然后将优化的 MLP 用于自主机器人的导航。曲线度量下的准确度和面积用于评估和比较度量。此外，还使用了两种广受好评的梯度下降算法，包括反向传播 (BP) 和 Levenberg Marquardt (LM) 来验证基于进化的 MLP 训练器获得的结果。据观察，在使用其他进化和梯度下降算法训练的 MLP 中，使用 MFO 开发的 MLP 是最健壮的。