Multi-Objective chimp Optimizer: An innovative algorithm for Multi-Objective problems

Khishe, Mohammad; Orouji, Niloofar; Mosavi, Mohammad Reza

doi:10.1016/j.eswa.2022.118734

Cited by 35 publications

(6 citation statements)

References 74 publications

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“…The CHOA methodology has demonstrated successful application in a wide range of optimization tasks, such as sonar target classification [61], [88], COVID-19 diagnosis [89], [90], optimization of photovoltaic modules [91], radar pulse reception [92], environmental economic dispatch optimization [93], wireless sensor optimization [94], and others. However, similar to numerous LS algorithms, the CHOA algorithm is prone to converging towards local minima, resulting in suboptimal solutions in specific strategies [95], [96]. In order to mitigate the problem of premature convergence and improve the efficacy of PP, researchers have explored the utilization of EPA.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Robot Path Planning Through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Zhang,

Zhang

2024

IEEE Access

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The importance of efficient path planning (PP) cannot be overstated in the domain of robots, as it involves the utilization of intelligent algorithms to determine the optimal trajectory for robot to navigate between two given points. The main target of PP is to determine potential trajectories for robot operating in a complex environment containing various obstacles. The implementation of these movements should facilitate robot in traversing a path without encountering any collisions, starting from its initial location and reaching the intended destination. In order to address the challenges associated with robot PP, this study applies the chimp optimization algorithm (CHOA) as a local searching (LS) technique and the evolutionary programming algorithm (EPA) to enhance the potential route discovered via a collection of LSs. In order to address CHOA's tendency to converge to local minima, a new updating technique called twin-reinforced (TR) is developed. In order to assess the effectiveness of the TRCHOA, we conducted a comparative analysis with other widely used meta-heuristic algorithms that are typically employed for solving robot PP problems. Additionally, we included the conventional probabilistic roadmap method (PRM) in our evaluation. We evaluated the planning performances of these algorithms on a standardized set of benchmark problems. Our findings indicate that the TRCHOA outperforms the other algorithms in terms of its planning performance. The evaluation of planning effectiveness encompasses several key criteria, namely path length, consistency of scheduled paths, time complexity, and rate of success. The experiments conducted in this study provide evidence of the statistically significant value of the enhancements obtained through the implementation of the proposed method. The findings derived from the TRCHOA provide compelling evidence of its capacity to accurately determine the most optimal route within the specified test map.INDEX TERMS Chimp optimization algorithm; evolutionary programming; path planning; robot; twinreinforced technique.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Robot Path Planning Through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Zhang,

Zhang

2024

IEEE Access

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“…The abovementioned drawbacks, on the one hand, and the no-free lunch principle, on the other, drove us to search for a combination strategy to concurrently enhance the exploration and exploitation phases so that the algorithm's convergence speed increases and does not become stuck in local optima (Khishe, 2023). CHOA was chosen above other recently discovered optimization algorithms due to its straightforward mathematics (Bo et al, 2023), limited number of parameter settings (Khishe et al, 2023), and versatility (Liu et al, 2022). Nevertheless, it frequently becomes stuck in a local optimum while attempting to resolve complex issues (Khishe et al, 2021;Saffari et al, 2022;Wang et al, 2021).…”

Section: Motivationsmentioning

confidence: 99%

“…CHOA was chosen above other recently discovered optimization algorithms due to its straightforward mathematics (Bo et al, 2023), limited number of parameter settings (Khishe et al, 2023), and versatility (Liu et al, 2022). Nevertheless, it frequently becomes stuck in a local optimum while attempting to resolve complex issues (Khishe et al, 2021; Liu, Li, et al, 2023; Saffari et al, 2022; Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Leveraging quantum‐inspired chimp optimization and deep neural networks for enhanced profit forecasting in financial accounting systems

Zhang,

Alsubai,

Alqahtani

et al. 2024

Expert Systems

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Deep learning and metaheuristic algorithms have recently increased in various sciences, including financial accounting information systems (FAISs). However, the existence of large datasets has dramatically increased the complexity of these hybrid networks, so to address this shortcoming, this paper aims to develop a quantum‐behaved chimp optimization algorithm (QCHOA) and deep neural network (DNN) for the prediction of the profit based on FAISs. Considering that there is no suitable dataset for the challenge, a novel dataset is developed utilizing the 15 features from the Chinese market dataset to compare more. This work designs QCHOA and five DNN‐based predictors to forecast profit. These algorithms include the universal learning CHOA (ULCHOA), the niching CHOA (NCHOA) as the two best‐modified versions of CHOA, the quantum‐behaved whale optimization algorithm (QWOA), and the quantum‐behaved grey wolf optimizer (QGWO) as the two best quantum‐behaved optimizers as well as classic CHOA. The most effective deep learning‐based predictors for forecasting the profit, ranked from highest to lowest, are DNN‐QCHOA, DNN‐NCHOA, DNN‐QWOA, DNN‐QGWO, DNN‐ULCHOA, DNN‐CHOA, and classic DNN, with corresponding ranking scores of 42, 36, 30, 24, 18, 12, and 6. As a final suggestion for profit prediction, the DNN‐CHOA is shown to be the most accurate model.

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“…These methods demonstrate advanced approaches in the categorization of feasible solutions. Additionally, the Multi-objective Chimp Optimizer proposed [ 24 ] offers insights into handling multiple objectives, which can be pertinent in distinguishing between feasible and non-feasible solutions. While this study's proposed method pertains exclusively to unconstrained challenges and thus a detailed review of constraint-handling techniques is omitted as well as earlier works [ [25] , [26] , [27] ], which further elaborate on the evolution and applications of chimp optimization algorithms in various contexts.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Kalita,

Naga Ramesh,

Čep

et al. 2024

Heliyon

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Multi-Objective chimp Optimizer: An innovative algorithm for Multi-Objective problems

Cited by 35 publications

References 74 publications

Enhancing Robot Path Planning Through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Enhancing Robot Path Planning Through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Leveraging quantum‐inspired chimp optimization and deep neural networks for enhanced profit forecasting in financial accounting systems

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

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