Improved Equilibrium Optimizer for Accurate Training of Feedforward Neural Networks

IF 1 Q4 OPTICS Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI:10.3103/s1060992x24700048

Seyed Sina Mohammadi, Mohammadreza Salehirad, Mohammad Mollaie Emamzadeh, Mojtaba Barkhordari Yazdi

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Abstract

One of the most demanding applications of accurate Artificial Neural Networks (ANN) can be found in medical fields, mainly to make critical decisions. To achieve this goal, an efficient optimization and training method is required to tune the parameters of ANN and to reach the global solutions of these parameters. Equilibrium Optimizer (EO) has recently been introduced to solve optimization problems more reliably than other optimization methods which have the ability to escape from the local optima solutions and to reach the global optimum solution. In this paper, to achieve a higher performance, some modifications are applied to the EO algorithm and the Improved Equilibrium Optimizer (IEO) method is presented which have enough accuracy and reliability to be used in crucial and accurate medical applications. Then, this IEO approach is utilized to learn ANN, and IEO-ANN algorithm will be introduced. The proposed IEO-ANN will be implemented to solve real-world medical problems such as breast cancer detection and heart failure prediction. The obtained results of IEO are compared with those of three other well-known approaches: EO, Particle Swarm Optimizer (PSO), Salp Swarm Optimizer (SSO), and Back Propagation (BP). The recorded results have shown that the proposed IEO algorithm has much higher prediction accuracy than others. Therefore, the presented IEO can improve the accuracy and convergence rate of tuning neural networks, so that the proposed IEO-ANN is a suitable classifying and predicting approach for crucial medical decisions where high accuracy is needed.

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改进平衡优化器，实现前馈神经网络的精确训练

摘要精确的人工神经网络（ANN）在医疗领域的应用最为广泛，主要用于做出关键决策。为实现这一目标，需要一种高效的优化和训练方法来调整人工神经网络的参数，并获得这些参数的全局解。与其他优化方法相比，均衡优化器（EO）具有摆脱局部最优解并达到全局最优解的能力，因此最近被引入用于更可靠地解决优化问题。为了实现更高的性能，本文对 EO 算法进行了一些修改，并提出了改进平衡优化器（IEO）方法，该方法具有足够的准确性和可靠性，可用于关键和精确的医疗应用。然后，利用这种 IEO 方法来学习 ANN，并介绍 IEO-ANN 算法。提出的 IEO-ANN 将用于解决现实世界中的医疗问题，如乳腺癌检测和心力衰竭预测。IEO 算法的结果将与其他三种著名方法的结果进行比较：EO、粒子群优化器（PSO）、萨尔普群优化器（SSO）和反向传播（BP）。记录结果表明，所提出的 IEO 算法的预测精度远远高于其他算法。因此，所提出的 IEO 可以提高调整神经网络的准确性和收敛速度，从而使所提出的 IEO-ANN 成为一种适用于需要高准确性的关键医疗决策的分类和预测方法。

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来源期刊

Optical Memory and Neural Networks OPTICS-

CiteScore

1.50

自引率

11.10%

发文量

期刊介绍： The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.