CICDWOA: A Collective Cognitive Sharing Whale Optimization Algorithm with Cauchy Inverse Cumulative Distribution for 2D/3D Path Planning and Engineering Design Problems

2026-03-18

AI Summary

This study addresses the premature convergence and insufficient population diversity commonly observed in the Whale Optimization Algorithm (WOA) by proposing an enhanced WOA framework. The proposed approach integrates a Good Nodes Set-based uniform initialization, a population cognitive sharing mechanism, and a spiral update strategy guided by the inverse cumulative distribution of the Cauchy distribution. Furthermore, it incorporates a nonlinear convergence factor and a hybrid Gaussian–Cauchy differential mutation operator to balance global exploration and local exploitation. Comprehensive experiments demonstrate that the enhanced algorithm achieves superior performance across 23 benchmark functions, 2D/3D path planning tasks, and 10 engineering design problems, attaining an average Friedman rank of 1.6790—outperforming both the original WOA and several state-of-the-art metaheuristic algorithms.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The Whale Optimization Algorithm (WOA) has shown strong optimization ability but still suffers from premature convergence and weak search diversity. To address these issues, this paper proposes an enhanced WOA variant called CICDWOA. The proposed algorithm introduces a Good Nodes Set (GNS) method for uniform population initialization, a Collective Cognitive Sharing (CCS) mechanism to enhance group collaboration, and an Enhanced Spiral Updating strategy based on the Cauchy Inverse Cumulative Distribution (CICD) to strengthen global exploration and local exploitation balance. In addition, a nonlinear convergence factor and a Hybrid Gaussian-Cauchy mutation based on Differential Evolution (DE) further improve convergence efficiency and population diversity. CICDWOA was evaluated on 23 benchmark functions, 2D robot path planning problems, 3D UAV path planning tasks and 10 engineering design problems. Statistical experiment results show that CICDWOA achieves faster convergence, higher accuracy, and better robustness than classical WOA and other advanced metaheuristic algorithms. CICDWOA gained average Friedman value of 1.6790, ranking first among the SOTA algorithms. And the results of engineering simulations confirm that CICDWOA provides an effective and general framework for solving complex optimization and engineering problems. The code of CICDWOA are available on \href{URL}{https://github.com/JunhaoWei-mpu/ROBIS-Lab/tree/CICDWOA}.

Problem

• Whale Optimization Algorithm
• premature convergence
• search diversity
• metaheuristic optimization

Innovation

• Whale Optimization Algorithm
• Collective Cognitive Sharing
• Cauchy Inverse Cumulative Distribution
• Path Planning
• Metaheuristic Optimization