Imitation-based Cognitive Learning Optimizer for solving numerical and engineering optimization problems

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research Pub Date : 2024-04-20 DOI:10.1016/j.cogsys.2024.101237

Sobia Tariq Javed , Kashif Zafar , Irfan Younas

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引用次数: 0

Abstract

A novel human cognitive and social interaction-based metaheuristic called Imitation-based Cognitive Learning Optimizer (CLO) is proposed and developed to solve engineering optimization problems effectively. CLO is inspired by humans’ imitation and social learning behavior during the life cycle. The human life cycle consists of various stages. Social and imitating human behavior during the life cycle is incorporated into this algorithm to improve cognitive abilities. The three real-world mechanical engineering optimization problems (Welded beam problem, Tension–Compression String Design Problem, and Speed reducer problem) and 100 challenging benchmark functions including uni-modal, multi-modal and CEC-BC-2017 functions are used for the real-time validation. CLO is compared with 12 state-of-art algorithms from the literature. The experiments along with convergence analysis and Friedman’s Mean Rank (FMR) statistical test show the superiority of CLO over the other chosen algorithms.

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基于模仿的认知学习优化器，用于解决数值和工程优化问题

为了有效解决工程优化问题，我们提出并开发了一种新颖的基于人类认知和社会互动的元启发式--基于模仿的认知学习优化器（CLO）。CLO 的灵感来源于人类在生命周期中的模仿和社会学习行为。人类的生命周期由多个阶段组成。该算法将人类在生命周期中的社会和模仿行为融入其中，以提高认知能力。三个真实世界的机械工程优化问题（焊接梁问题、张力-压缩弦设计问题和减速机问题）和 100 个具有挑战性的基准函数（包括单模态、多模态和 CEC-BC-2017 函数）被用于实时验证。CLO 与文献中的 12 种先进算法进行了比较。实验以及收敛性分析和弗里德曼均值秩（FMR）统计检验表明，CLO 优于其他所选算法。

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来源期刊

Cognitive Systems Research 工程技术-计算机：人工智能

CiteScore

9.40

自引率

5.10%

发文量

审稿时长

>12 weeks

期刊介绍： Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial. The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition. Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.