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An improved pathfinder algorithm (ASDR-PFA) based on adaptation of search dimensional ratio for solving global optimization problems and optimal feature selection

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Abstract

Pathfinder algorithm (PFA) is a recently introduced meta-heuristic technique that mimics the cooperative behavior of animal groups in search of the best food area. PFA consists of two phases, namely, the path-finder phase and the follower phase. The former explores new search regions through its versatile explorative power, while in the latter stage, followers change their position by tracking the leader and using their perception. However, PFA is prone to falling into local optima, leading to a slow convergence rate while dealing with high-dimensional ill-conditioned problems. Therefore, this article proposes an improved PFA called ASDR-PFA based on an adaptation of the search dimensional ratio (ASDR) to address the issues in PFA. The proposed method incorporates an ASDR concept that uses a search dimensional ratio (SDR) parameter to generate new candidate solutions using the existing global best. Its strength lies in its dynamic updating of the SDR parameter, which further tunes the balance between exploration and exploitation processes. As a result, the convergence rate of PFA is enhanced. The effectiveness of ASDR-PFA is verified using a set of 16 basic benchmark functions and IEEE-CEC-2011 and IEEE-CEC-2017 problem suites. Wilcoxon's signed rank test is also conducted to confirm its statistical significance. Additionally, ASDR-PFA is utilized to tackle some optimal feature selection problems to substantiate its applicability. Too, a comparative assessment of empirical outcomes attained through ASDR-PFA and some modern meta-heuristics is carried out to showcase its suitability level.

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Data availability

The datasets analyzed during the current study are available in the UCI and KAGGLE repositories. Dataset name–Dataset link, Sonar–https://www.kaggle.com/datasets/freddymeriwether/sonar-data Spam-base–https://archive.ics.uci.edu/ml/datasets/Spambase Hill-Valley Data Set–https://archive.ics.uci.edu/ml/datasets/Hill-Valley Malware Executable Detection–https://www.kaggle.com/datasets/piyushrumao/malware-executable-detection Parkinson's Diseases Classification–https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+Classification QSAR androgen receptor–https://archive.ics.uci.edu/ml//datasets/QSAR+androgen+receptor.

Abbreviations

HCEF:

High-conditioned elliptic function

EGRF:

Expanded Griewank's plus Rosenbrock's function

ESF6:

Expanded Scaffer's F6 function

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Funding

The authors did not receive support from any organization for the submitted work. No funds, grants, or other support were received.

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Authors and Affiliations

  1. Department of CSIT, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to Be) University, Bhubaneswar, India

    Ajit Kumar Mahapatra

  2. School of Computer Engineering, KIIT Deemed to Be University, Bhubaneswar, Odisha, 751024, India

    Nibedan Panda

  3. Department of CSE, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to Be) University, Bhubaneswar, India

    Binod Kumar Pattanayak

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  1. Ajit Kumar Mahapatra
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AKM: Conceptualization, Methodology, Software, Analysis, Writing NP: Conceptualization, Methodology, Analysis, Review, Writing and Editing, Supervision BKP: Review, Editing and General Supervision.

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Correspondence to Nibedan Panda.

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Mahapatra, A.K., Panda, N. & Pattanayak, B.K. An improved pathfinder algorithm (ASDR-PFA) based on adaptation of search dimensional ratio for solving global optimization problems and optimal feature selection. Prog Artif Intell 12, 323–348 (2023). https://doi.org/10.1007/s13748-023-00306-9

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