Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems

Feng, Zhong-kai; Niu, Wen-jing; Liu, Shuai

doi:10.1016/j.asoc.2020.106734

Cited by 133 publications

(64 citation statements)

References 63 publications

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“…It is worth mentioning that there are other recently developed metaheuristic algorithms motivated by humanbased techniques, some of the famous ones include: Farmland Fertility (FF) algorithm inspired the nature of farmland fertility [227], Queuing Search (QS) algorithm inspired the natural human activities in queuing [228], Supply Demand-based Optimization (SDO) method mimics the demand relation of consumers and supply relation of producers [229], Gaining Sharing Knowledge-based algorithm (GSK) mimics the natural process of gaining and sharing knowledge between human during their life time [230], Interactive Autodidactic School (IAS) technique mimics the basis of interaction occurs among students of autodidactic school with the aim of acquiring new knowledge through a combination of self-teaching, group discussion, criticism, and competition [231], Group Teaching Optimization Algorithm (GTOA) inspired by group teaching mechanism in nature [232], Adolescent Identity Search Algorithm (AISA) inspired the natural identity behavior of adolescents in a peer group [233], Dynastic Optimization Algorithm (DOA) mimics the social behavior in human dynasties by nature [234], Color Harmony Algorithm (CHA) is art-inspired that models its search behavior based on harmonic colors [235], Student Psychology Based Optimization (SPBO) mimics the natural thinking of students with efforts to improve their performance in examination to become the best student in the class [236], Search and Rescue optimization algorithm (SAR) inspired the natural behavior of humans for the time of search and rescue operations [236], Tiki-Taka Algorithm (TTA) mimics the football playing style [237], Cooperation Search Algorithm (CSA) inspired the natural cooperation behaviors in team [238], Battle Royale Optimization (BRO) inspired by a variety of digital game skills [239], and so on. Keeping in view of the efficiency of these newly human-based algorithms, they are yet to be adopted in t-way testing for combinatorial optimization problem.…”

Section: Human-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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Section: Human-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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“…The HGSPSO can be suggested to be implemented in reservoir optimisation. Besides that, the harmony search [ 122 ] and the cooperation search algorithms [ 123 ] have also been widely been utilised for the MO engineering optimisation problem. However, as of now, both algorithms have been little used in very few research works in optimisation of reservoir operation.…”

Section: Other Mhasmentioning

confidence: 99%

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Lai

Huang

Koo

et al. 2022

Arch Computat Methods Eng

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Reservoir operation optimisation secures benefits, such as optimising energy production while minimising the possibility of flooding, operating costs, and water scarcity, at the lowest possible cost. This paper carries reviews of research on reservoir optimisation models and the consequential challenges of optimally operating reservoir operations. An introductory section is given to the background of reservoir operations and the current concerns on the optimal reservoir operations, for the decision-makers and stakeholders. Next, the review covered the recent ten years (between 2011 and 2021), on the recent research developments in innovation and techniques of reservoir operation optimisation. Further reviews on the conventional techniques that are the traditional methods, linear programming, nonlinear programming, and dynamic programming are discussed. Enhancements to the techniques in improving the drawbacks of the traditional techniques in optimisation of reservoir policies are next explained and evaluated. Recent advances in applying metaheuristics optimisation algorithms beneficial to the reservoir operations are explained, including the advantages and hinderances. A comprehensive tabulated and categorised review according to the classification of reservoir models, evaluation methods, and reservoir systems is given.

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“…[47] proposed an optimization methodology through parallel computing and swarm intelligence. Regrading to the numerical optimization method proposed in [48], we analyze the optimization performance to MLP. From the results, we could find the enhanced dense connected convolutional blocks perform better than other combinations.…”

Section: Ablation Studymentioning

confidence: 99%

Multi-Head Attentional Point Cloud Classification and Segmentation Using Strictly Rotation-Invariant Representations

et al. 2021

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Point cloud processing plays an increasingly essential role in three-dimensional (3D) computer vision target, scene parsing, environmental perception, etc. Compared with using aligned point cloud data for classification and segmentation, the strictly rotation-invariant representations show enough robustness. Inspired by the great success of deep learning, we propose a novel neural network called Multi-head Attentional Point Cloud Classification and Segmentation Using Strictly Rotation-invariant Representations. Our research focuses on processing the point cloud rotated in any direction effectively and precisely. First of all, the strictly rotation-invariant point cloud representations are obtained through point projection. Then we apply a multi-head attentional convolution layer (MACL) using attention coding to develop the performance of point cloud feature extraction. Finally, our network assigns different responses and recognizes the overall geometry well through a key point descriptor, adding to the global feature. Our method can explore more in-depth information for accuracy enhancement with attention pooling and multi-layer perceptron (MLP) based on an advanced DenseNet. Our network enjoys 90.63% and 87.50% classification accuracy testing on ModelNet10 and ModelNet40, and 75.15% intersection over union metric (mIoU) evaluating on ShapeNet Part dataset, remaining under any rotation. Rotating experimental results indicate that our framework realizes better point cloud classification and segmentation performance than most state-of-the-art methods.

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Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems

Cited by 133 publications

References 63 publications

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Multi-Head Attentional Point Cloud Classification and Segmentation Using Strictly Rotation-Invariant Representations

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