A Voting-Mechanism-Based Ensemble Framework for Constraint Handling Techniques

Wu, Guohua; Wen, Xupeng; Pedrycz, Witold; Suganthan, Ponnuthurai Nagaratnam

doi:10.1109/tevc.2021.3110130

Cited by 46 publications

(4 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, ATM [36], C2ODE [37] ,FROFI [38] and VMCH [39] are four different kinds of evolutionary algorithms for the mixedvariable problems. In particular, the C2ODE and FROFI algorithms are designed for continuous problems, and we have modified them to address discrete problems, renaming them as dC2ODE and dFROFI, respectively.…”

Section: B Simulation Resultsmentioning

confidence: 99%

A Novel Spaceborne SAR Constellation Scheduling Algorithm for Sea Surface Moving Target Search Tasks

Liu,

Chang,

Deng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

With the expanding scope of human activities in marine environments, the efficient detection and tracking of mobile targets on the ocean's surface have become increasingly crucial. SAR constellation can obtain ground observation data based on user requests and subject to visibility conditions. Now it is an indispensable tool in sea surface moving target search Tasks. Satellite constellation resources are scarce and limited, and user demands are diverse. How to rationally dispatch satellite constellation resources to meet user needs to the maximum extent and improve the application efficiency of satellite resources is an urgent scientific problem that needs to be solved. This paper mainly expounds two respects of work: firstly, modeling SAR constellation scheduling problem for sea surface moving target search tasks to establish the objective function; secondly, a novel multistrategy discrete constrained differential evolution algorithm denoted as MSDCDE is proposed in the paper. The proposed MSDCDE algorithm integrates cross strategy based on discrete variables, constraint handling techniques, population restart strategy, and leftshift local strategy, which can effectively avoid falling into local optimality, thereby achieving global optimality and improving search and rescue performance. Six sets of experiments, totaling 215 runs, have been conducted to validate the effectiveness of the proposed resolution process framework and the MSDCDE algorithm. The proposed method demon strated an over 48.98% performance improvement compared to some stateoftheart algorithms and significantly reduced task completion time.

show abstract

Section: B Simulation Resultsmentioning

confidence: 99%

A Novel Spaceborne SAR Constellation Scheduling Algorithm for Sea Surface Moving Target Search Tasks

Liu,

Chang,

Deng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Thus, our future research will focus on integrating surrogate-assisted approaches [32]- [34] into the i-ACLA-CGP framework, wherein surrogates are utilized as a recommendation system while learning the user preferences. Moreover, based on recent insight regarding a voting approach to evolutionary algorithms [25], [26] and constraint handling techniques [60], [61], we may further boost our approach by utilizing such methods to capture more complex user preferences. These techniques may be useful if we suppose the user preferences as constraints that the loading algorithms must satisfy.…”

Section: Discussionmentioning

confidence: 99%

“…Note that powerful modern optimizers exist that may further boost the performance of i-ACLA-CGP, including the adaptation, ensemble, and voting mechanism of evolutionary algorithms [25]- [31], as well as surrogate-assisted approaches [32]- [34]. However, this paper focuses on demonstrating the feasibility of the automatic generation of loading algorithms through interactive optimization frameworks, and we leave possible improvements of i-ACLA-CGP as a topic of future study (see also Section VII).…”

Section: Introductionmentioning

confidence: 99%

Automatic Construction of Loading Algorithms With Interactive Genetic Programming

et al. 2022

View full text Add to dashboard Cite

The design of a freight loading pattern is often conducted by skilled workers, who handle unquantifiable objectives and/or preferences. Our previous study presented an automatic construction technique for loading algorithms using genetic programming-based hyper-heuristics; however, this technique is only applicable to fully quantifiable loading problems. Thus, the approach described in this paper integrates an interactive framework with users into our previous technique to automatically construct algorithms that derive loading patterns adapted to user objectives and/or preferences. Thus, once a loading algorithm has been derived with user interactions, it can be reused to obtain the preferred loading patterns on other problems without any additional interactions. Experimental results show that the proposed algorithm can produce loading algorithms adapted for user preferences under a limit of 50 human interactions. Further, we also show that the derived loading algorithms can be applicable to different loading situations without any additional user interactions. Thus, these observations suggest the benefit of our approaches in reducing the burden placed on skilled workers for practical LPD tasks.

show abstract

“…high-level methodologies, are not dependent on the problem being solved, making them applicable to a large number of problem types [1,2]. The main reason for this is that when traditional methods are employed to solve a problem, the problem becomes more complex as the scale of the problem increases, necessitating a considerable amount of processing power and time [3,4,5,6,7,8]. Metaheuristic algorithms reduce the time it takes to identify the best solution or the solution that most suitable.…”

Section: Introductionmentioning

confidence: 99%

Senior Learning JAYA With Powell’s Method and Incremental Population Strategy

Yavuz

2022

IEEE Access

View full text Add to dashboard Cite

JAYA algorithm is one a recently developed meta-heuristic algorithm that does not require algorithm-specific parameters. It is an algorithm based on the fact that the solutions always go towards the best when searching. This paper proposes a JAYA variant (JAYA-SIP) with three improvements to the Original JAYA algorithm. It has incorporated the senior learning strategy, the incremental population strategy, and Powell's local search method into JAYA. The improvements were tested with IEEE Congress on Evolutionary Computation (CEC) benchmark set for 30 and 50 dimensions, and the benchmark functions set from a special issue of the Soft Computing journal (SOCO) for 500 and 1000 dimensions. In addition to benchmark sets, the performance of JAYA-SIP was evaluated with nine CEC 2011 real-world test functions. The results of the proposed algorithm are compared with JAYA variants and some meta-heuristic algorithms. According to the results of the experiment and the analysis, the proposed improvements increased the performance of the JAYA algorithm. JAYA-SIP achieved better results than the other algorithms it was compared with.

show abstract

A Voting-Mechanism-Based Ensemble Framework for Constraint Handling Techniques

Cited by 46 publications

References 56 publications

A Novel Spaceborne SAR Constellation Scheduling Algorithm for Sea Surface Moving Target Search Tasks

A Novel Spaceborne SAR Constellation Scheduling Algorithm for Sea Surface Moving Target Search Tasks

Automatic Construction of Loading Algorithms With Interactive Genetic Programming

Senior Learning JAYA With Powell’s Method and Incremental Population Strategy

Contact Info

Product

Resources

About