1
|
Nematzadeh H, García-Nieto J, Navas-Delgado I, Aldana-Montes JF. Ensemble-based genetic algorithm explainer with automized image segmentation: A case study on melanoma detection dataset. Comput Biol Med 2023; 155:106613. [PMID: 36764157 DOI: 10.1016/j.compbiomed.2023.106613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/05/2023] [Accepted: 01/28/2023] [Indexed: 02/07/2023]
Abstract
Explainable Artificial Intelligence (XAI) makes AI understandable to the human user particularly when the model is complex and opaque. Local Interpretable Model-agnostic Explanations (LIME) has an image explainer package that is used to explain deep learning models. The image explainer of LIME needs some parameters to be manually tuned by the expert in advance, including the number of top features to be seen and the number of superpixels in the segmented input image. This parameter tuning is a time-consuming task. Hence, with the aim of developing an image explainer that automizes image segmentation, this paper proposes Ensemble-based Genetic Algorithm Explainer (EGAE) for melanoma cancer detection that automatically detects and presents the informative sections of the image to the user. EGAE has three phases. First, the sparsity of chromosomes in GAs is determined heuristically. Then, multiple GAs are executed consecutively. However, the difference between these GAs are in different number of superpixels in the input image that result in different chromosome lengths. Finally, the results of GAs are ensembled using consensus and majority votings. This paper also introduces how Euclidean distance can be used to calculate the distance between the actual explanation (delineated by experts) and the calculated explanation (computed by the explainer) for accuracy measurement. Experimental results on a melanoma dataset show that EGAE automatically detects informative lesions, and it also improves the accuracy of explanation in comparison with LIME efficiently. The python codes for EGAE, the ground truths delineated by clinicians, and the melanoma detection dataset are available at https://github.com/KhaosResearch/EGAE.
Collapse
Affiliation(s)
- Hossein Nematzadeh
- ITIS Software, Universidad de Málaga, Arquitecto Francisco Peñalosa 18, Malaga, 29071, Spain; Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Malaga, Spain.
| | - José García-Nieto
- ITIS Software, Universidad de Málaga, Arquitecto Francisco Peñalosa 18, Malaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Malaga, Spain; Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Malaga, Spain.
| | - Ismael Navas-Delgado
- ITIS Software, Universidad de Málaga, Arquitecto Francisco Peñalosa 18, Malaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Malaga, Spain; Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Malaga, Spain.
| | - José F Aldana-Montes
- ITIS Software, Universidad de Málaga, Arquitecto Francisco Peñalosa 18, Malaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Malaga, Spain; Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Malaga, Spain.
| |
Collapse
|
2
|
Efficient text document clustering approach using multi-search Arithmetic Optimization Algorithm. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108833] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
3
|
Chen Y, Liang J, Wu Y, He B, Lin L, Wang Y. Self-Regulating and Self-Perception Particle Swarm Optimization with Mutation Mechanism. J INTELL ROBOT SYST 2022. [DOI: 10.1007/s10846-022-01627-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
4
|
Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization. MATHEMATICS 2022. [DOI: 10.3390/math10101620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) method to effectively tackle complicated optimization problems. Specifically, for each particle, a new promising exemplar is constructed by letting its personal best position cognitively learn from a better personal experience randomly selected from those of others based on a novel predominant cognitive learning strategy. As a result, different particles preserve different guiding exemplars. In this way, the learning effectiveness and the learning diversity of particles are expectedly improved. To eliminate the dilemma that PCLPSO is sensitive to the involved parameters, we propose dynamic adjustment strategies, so that different particles preserve different parameter settings, which is further beneficial to promote the learning diversity of particles. With the above techniques, the proposed PCLPSO could expectedly compromise the search intensification and diversification in a good way to search the complex solution space properly to achieve satisfactory performance. Comprehensive experiments are conducted on the commonly adopted CEC 2017 benchmark function set to testify the effectiveness of the devised PCLPSO. Experimental results show that PCLPSO obtains considerably competitive or even much more promising performance than several representative and state-of-the-art peer methods.
Collapse
|
5
|
Zhou X, Zhou S, Han Y, Zhu S. Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5241-5268. [PMID: 35430863 DOI: 10.3934/mbe.2022246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In the traditional particle swarm optimization algorithm, the particles always choose to learn from the well-behaved particles in the population during the population iteration. Nevertheless, according to the principles of particle swarm optimization, we know that the motion of each particle has an impact on other individuals, and even poorly behaved particles can provide valuable information. Based on this consideration, we propose Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization, called LFIACL-PSO. In the LFIACL-PSO algorithm, First, when the particle is trapped in the local optimum and cannot jump out, inverse learning is used, and the learning step size is obtained through the Lévy flight. Second, to increase the diversity of the algorithm and prevent it from prematurely converging, a comprehensive learning strategy and Ring-type topology are used as part of the learning paradigm. In addition, use the adaptive update to update the acceleration coefficients for each learning paradigm. Finally, the comprehensive performance of LFIACL-PSO is measured using 16 benchmark functions and a real engineering application problem and compared with seven other classical particle swarm optimization algorithms. Experimental comparison results show that the comprehensive performance of the LFIACL-PSO outperforms comparative PSO variants.
Collapse
Affiliation(s)
- Xin Zhou
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| | - Shangbo Zhou
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| | - Yuxiao Han
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| | - Shufang Zhu
- College of Computer Science, Chongqing University, Chongqing 400044, China
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400030, China
| |
Collapse
|
6
|
|
7
|
Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design. MATHEMATICS 2022. [DOI: 10.3390/math10020276] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, this paper proposed a new variant SOA based on individual disturbance (ID) and attraction-repulsion (AR) strategy, called IDARSOA, which employed ID to enhance the ability to jump out of local optimum and adopted AR to increase the diversity of population and make the exploration of solution space more efficient. The effectiveness of the IDARSOA has been verified using representative comprehensive benchmark functions and six practical engineering optimization problems. The experimental results show that the proposed IDARSOA has the advantages of better convergence accuracy and a strong optimization ability than the original SOA.
Collapse
|
8
|
Khan TA, Ling SH. A novel hybrid gravitational search particle swarm optimization algorithm. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021; 102:104263. [DOI: 10.1016/j.engappai.2021.104263] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
|
9
|
|
10
|
Huang L, Liao Q, Qiu R, Liang Y, Long Y. Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19. APPLIED ENERGY 2021; 283:116339. [PMID: 33753961 PMCID: PMC7969145 DOI: 10.1016/j.apenergy.2020.116339] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/14/2020] [Accepted: 11/21/2020] [Indexed: 05/03/2023]
Abstract
With the coronavirus pandemic wreathing havoc around the world, power industry has been hit hard due to the proposal of lockdown policies. However, the impact of lockdowns and shutdowns on the power system in different regions as well as periods of the pandemic can hardly be reflected on the foundation of current studies. In this paper, a prediction-based analysis method is developed to point out the electricity consumption gap resulted from the pandemic situation. The core of this method is a novel optimized grey prediction model, namely Rolling IMSGM(1,1) (Rolling Mechanism combined with grey model with initial condition as Maclaurin series), which achieves better prediction results in the face of long-term emergencies. A novel initial condition is adopted to track data with various characteristics in the form of higher-order polynomials, which are then determined by intelligent algorithms to realize accurate fitting. Historical power consumption data in China are utilized to carry out the monthly forecasts during COVID-19. Compared with other competitive models' prediction results, the superiority of IMSGM(1,1) are demonstrated. Through analyzing the gap between predicted consumption values and the actual data, it can be found that the impact of the pandemic on electricity varies in different periods, which is related to its severity and the local lockdown policies. This study helps to understand the impact on power industry in the face of such an emergency intuitively so as to respond to possible future events.
Collapse
Affiliation(s)
- Liqiao Huang
- National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, PR China
| | - Qi Liao
- National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, PR China
| | - Rui Qiu
- National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, PR China
| | - Yongtu Liang
- National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing 102249, PR China
| | - Yin Long
- Institute for Future Initiatives, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| |
Collapse
|
11
|
Maciel C. O, Cuevas E, Navarro MA, Zaldívar D, Hinojosa S. Side-Blotched Lizard Algorithm: A polymorphic population approach. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.106039] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
|
12
|
Liao Q, Zhang H, Xia T, Chen Q, Li Z, Liang Y. A data-driven method for pipeline scheduling optimization. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2019.01.017] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
13
|
Layout optimization of large-scale oil–gas gathering system based on combined optimization strategy. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.12.021] [Citation(s) in RCA: 129] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|