Histogram of non-zero demands. by Ferhat Yuna (21519851)

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SA Solutions. by Ferhat Yuna (21519851)

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Differences between FFVTD at Stage II-A and Stage II-B. by Ferhat Yuna (21519851)

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Flow of the inventory model with the proposed Markov approach. by Ferhat Yuna (21519851)

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TSA Solutions. by Ferhat Yuna (21519851)

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Transition probability matrix of 250-period demand. by Ferhat Yuna (21519851)

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Flowchart of intermittent demand generation with Markov chain. by Ferhat Yuna (21519851)

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Probability distributions of the test data and transition probabilities. by Ferhat Yuna (21519851)

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Diagram of transition probabilities for 250 demands. by Ferhat Yuna (21519851)

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Research methodology in this research. by Ferhat Yuna (21519851)

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The parameters determined as a result of the experimental designs. by Ferhat Yuna (21519851)

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Hypervolume (HV) comparison of six algorithms—MOPSO, rNSGA-II, IM-MOEA/D, MOEA/D-FRR-MAB, dMOPSO, and QLNSGA-II—on benchmark test suites. by Zhongfeng Li (3965060)

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Averaged absorption spectra as a function of cost compared to the far-red adapted cyanobacteria <i>Acarychloris marina</i> [62] for (A) 40% of available visible light below 700nm,... by Callum Gray (2691769)

Standard benchmark functions. by Yanzhao Gu (21192659)

“…The experimental results showed that GWOA achieved better convergence speed and solution accuracy than other algorithms in most test functions, especially in multimodal and compositional optimization problems, with an Overall Efficiency (OE) value of 74.46%. …”

Ave and Std of different algorithms. by Yanzhao Gu (21192659)

“…The experimental results showed that GWOA achieved better convergence speed and solution accuracy than other algorithms in most test functions, especially in multimodal and compositional optimization problems, with an Overall Efficiency (OE) value of 74.46%. …”

Parameter settings for metaheuristic algorithm. by Yanzhao Gu (21192659)

“…The experimental results showed that GWOA achieved better convergence speed and solution accuracy than other algorithms in most test functions, especially in multimodal and compositional optimization problems, with an Overall Efficiency (OE) value of 74.46%. …”

Inverted Generational Distance (IGD) comparison of six algorithms—MOPSO, rNSGA-II, IM-MOEA/D, MOEA/D-FRR-MAB, dMOPSO, and QLNSGA-II—on benchmark test suites. by Zhongfeng Li (3965060)

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Runtime (s) comparison of QLNSGAII against MOPSO, rNSGAII, IMMOEAD, MOEADFRRMAB, and dMOPSO on WFG and UF test functions. by Zhongfeng Li (3965060)

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Circulatory system-based optimization algorithm with dynamic penalty function for optimum design of large-scale water distribution networks by Veli Batmaz (20800175)

“…<p>Water distribution networks (WDNs) are a crucial component of urban infrastructure, and their optimization plays a vital role in minimizing construction costs. However, existing heuristic algorithms for WDN optimization often rely on specific parameters, limiting their performance and hindering their applicability to diverse network configurations, particularly in large-scale systems. …”

Computational cost of the analyses. by Raúl López-Martín (21386066)