A many-objective Jaya algorithm for many-objective optimization problems

Mane, Sandeep U.; Narsingrao, M. R.; Patil, Vrushali C.

doi:10.5267/j.dsl.2017.11.001

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…The efficacy of the MaOSCA optimizer was measured through various quality indicators. The outcomes of the MaOSCA algorithm were contrasted with four cutting-edge optimization techniques, including the NSGAIII [2], MOEADDE [14], MaOPSO [15], and MaOJAYA [16]. The upcoming subsection provides a concise overview of the selected test problems and performance measures.…”

Section: Evaluation and Interpretation Of Resultsmentioning

confidence: 99%

“…Unlike many optimization algorithms, the MaOSCA recognizes the importance of this information and retains data from previous populations. First, the MaOSCA algorithm is compared to the NSGA-III [2], MOADDE [14], MaOPSO [15], and MaOJAYA [16] on DTLZ1-DTLZ7 [17] problems. The experimental results indicate that the MaOSCA outperforms several cutting-edge multi-objective algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

Narayanan¹,

Ganesh

Čep

et al. 2023

Mathematics

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In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm’s capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA’s performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm.

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Section: Evaluation and Interpretation Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

Narayanan¹,

Ganesh

Čep

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…In [102], the authors presented a new modified version of the JAYA algorithm to optimize many-objective optimization problems (MaOPs). Their approach is named a many-objective JAYA (MaOJaya) algorithm.…”

Section: Other Modified Versions Of Jaya Algorithmmentioning

confidence: 99%

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Zitar

Al-Betar

Awadallah

et al. 2021

Arch Computat Methods Eng

125

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In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.

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Content-Based Medical Image Retrieval System for Skin Melanoma Diagnosis Based on Optimized Pair-Wise Comparison Approach

2022

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A many-objective Jaya algorithm for many-objective optimization problems

Cited by 9 publications

References 25 publications

A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

Content-Based Medical Image Retrieval System for Skin Melanoma Diagnosis Based on Optimized Pair-Wise Comparison Approach

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