Political optimizer with interpolation strategy for global optimization

Zhu, Aijun; Gu, Zhanqi; Hu, Cong; Niu, Junhao; Xu, Chuanpei; Li, Zhi

doi:10.1371/journal.pone.0251204

Cited by 14 publications

(4 citation statements)

References 68 publications

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“…Basetti et al (2021) proposed a Quasi-Oppositional-Based Political Optimizer (QOPO) by incorporating quasi-opposition-based learning (QOBL) (Tizhoosh, 2005) to improve the exploration and convergence capability of the political optimizer and utilized it to solve Economic Emission Load Dispatch Problem with Valve-Point Loading. Zhu et al (2021) proposed seven variants of PO, with different interpolation and refraction learning strategies. Xu et al (2022) proposed an improved political optimizer, namely the Quantum Nelder-Mead Political Optimizer (QNMPO), to solve performance optimization in photovoltaic systems.…”

Section: Parliamentarymentioning

confidence: 99%

Socio-inspired evolutionary algorithms: a unified framework and survey

2023

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Evolutionary computation has gone through vast and diverse research endeavors in the past few decades. Although the initial inspiration came from Darwin's ideas of biological evolution, the field has moved thereon to ideas from the collective intelligence of insects, birds, and fish, to name a few. A variety of algorithms have been proposed in the literature based on these ideas and have been shown to perform well in various applications. More recently, inspiration from human behavior and knowledge exchange and transformation has given rise to a new evolutionary computing paradigm. It is well recognized that human societies and problem-solving capabilities have evolved much faster than biological evolution. Many research endeavors have been reported in the literature inspired by diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in a plethora of algorithms worded differently from each other, but the underlying mechanisms could be more or less similar, causing immense confusion for a new reader. This paper presents a generalized framework for these Socio-inspired Evolutionary Algorithms (SIEAs) or Socio-inspired Metaheuristic Algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description of each SIEA enables a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. In that sense, this paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.

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Section: Parliamentarymentioning

confidence: 99%

Socio-inspired evolutionary algorithms: a unified framework and survey

2023

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“…PO-Based Parameter Optimization. Finally, the PO algorithm is applied for optimal adjustment of the parameters contained in the CFNN model [18,19]. PO approach is encouraged by b, the western political optimization algorithm which comprises 2 features.…”

Section: The Proposed Modelmentioning

confidence: 99%

A Hybrid Model for Intrusion Detection in IoT Applications

Alghamdi

2022

Wireless Communications and Mobile Computing

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Internet of Things (IoT) networks has recently become an important component of smart cities, smart buildings, health care, and other applications. It finds it beneficial due to the inherent characteristics of low cost, compact, and low-powered IoT devices. At the same time, security remains a challenging issue in the design of IoT networks. Intrusion detection systems (IDS) can be used to identify the occurrence of intrusions in the network, i.e., abnormal activities in the network. The latest advances in machine learning (ML) and metaheuristics can be employed to design effective IDS models for IoT networks. This article develops a novel political optimizer with cascade forward neural network (PO-CFNN-)-based IDS in the IoT environment. The major intention of the PO-CFNN technique is to determine the occurrence of intrusions from the IoT environment. The PO-CFNN technique follows three major processes, namely, preprocessing, classification, and parameter optimization. Initially, the networking data is preprocessed to transform it into a useful format. Following that, the CFNN technique is employed for the identification and classification of intrusions in the IoT environment. In the final stage, the PO algorithm is applied for the optimal adjustment of the parameters involved in the CFNN model. The experimental validation of the PO-CFNN technique on a benchmark dataset stated the better outcomes of the PO-CFNN technique over recent approaches.

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“…Askari et al modified each stage of PO to improve the exploration ability and balance of the algorithm because it is found PO prematurely converges for complex problems [34]. Zhu et al integrated PO with quadratic interpolation, advanced quadratic interpolation, cubic interpolation, Lagrange interpolation, Newton interpolation, and refraction learning, and proposed a sequence of novel PO variants [35].…”

Section: Introductionmentioning

confidence: 99%

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

Dong

Zou

et al. 2022

Preprint

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This paper proposes a novel hybrid optimization algorithm named GPOFWA, which integrates political optimizer (PO) with fireworks algorithm (FWA) to solve numerical and engineering optimization problems. The original PO uses subgroup optimal solutions such as party leaders and constituency winners to guide the movement of the search agent. However, the number of such subgroup optimal solutions is limited, which leads to insufficient global exploration capabilities of PO. In addition, the recent past-based position updating strategy (RPPUS) of PO lacks effective verification of the updated candidate solutions, which reduces the convergence speed of the algorithm. The proposed hybrid algorithm uses the spark explosion mechanism in FWA to perform explosion spark and Gauss explosion spark operations on the subgroup optimal solutions (party leader and constituency winner) respectively based on the greedy strategy, which optimizes the subgroup optimal solution and enhances the exploitative ability of the algorithm. Moreover, Gaussian explosion sparks are also used to correct the candidate solutions after RPPUS, which makes up for the shortcomings of the original PO. In addition, a new subgroup optimal solution called the Converged Mobile Center (CMC) based on two-way consideration is designed to guide the movement of search agents. We test the presented hybrid algorithm on 30 well-known benchmark test functions and three engineering optimization problems. The experimental results show that GPOFWA is superior to nine state-of-the-art methods in terms of the quality of the resulting solution.

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Political optimizer with interpolation strategy for global optimization

Cited by 14 publications

References 68 publications

Socio-inspired evolutionary algorithms: a unified framework and survey

Socio-inspired evolutionary algorithms: a unified framework and survey

A Hybrid Model for Intrusion Detection in IoT Applications

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

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