SPEA2 based on grid density search and elite guidance for multi-objective operation optimization of wastewater treatment process

Zhou, Ping; Liu, Hongpeng; Chai, Tianyou

doi:10.1016/j.asoc.2023.110529

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…The goal of this strategy is to dynamically adjust the neighborhood size based on factors such as the convergence level within the neighborhood, the state of the population, and historical neighborhood information, ensuring that it meets the requirements of any stage of population iteration and evolution [15]. The ES-SPEA2-DD was evaluated against grid Density Search and Elite Guidance Strength Pareto Evolutionary Algorithm 2(GDSEG-SPEA2) [16]. It employs a sophisticated methodology that combines grid density search with elite guidance strategies to enhance both the diversity of solutions and convergence towards the Pareto front.The next section details a robustness comparison table that highlights the strength of the algorithms against features common to them.…”

mentioning

confidence: 99%

An Agile Approach for Adopting Sustainable and Environmentally Friendly Energy Solutions with Advanced Computational Techniques

Konneh,

Howlader,

Elkholy

et al. 2024

Preprint

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In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

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mentioning

confidence: 99%

An Agile Approach for Adopting Sustainable and Environmentally Friendly Energy Solutions with Advanced Computational Techniques

Konneh,

Howlader,

Elkholy

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This ensures it meets the requirements at any stage of population iteration and evolution [41]. The ES-SPEA2-DD was evaluated against the Grid Density Search and Elite Guidance Strength Pareto Evolutionary Algorithm (GDSEG-SPEA2) [42]. It employs a sophisticated methodology that combines grid density search with elite guidance strategies to enhance solution diversity and convergence towards the Pareto front.…”

mentioning

confidence: 99%

An Agile Approach for Adopting Sustainable Energy Solutions with Advanced Computational Techniques

Konneh,

Howlader,

Elkholy

et al. 2024

Energies

View full text Add to dashboard Cite

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

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Multiobjective optimization of perforation design with mechanism learning to increase the stimulated reservoir volume of unconventional reservoirs

Zhang,

Zeng,

Guo

et al. 2025

Geoenergy Science and Engineering

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SPEA2 based on grid density search and elite guidance for multi-objective operation optimization of wastewater treatment process

Cited by 7 publications

References 36 publications

An Agile Approach for Adopting Sustainable and Environmentally Friendly Energy Solutions with Advanced Computational Techniques

An Agile Approach for Adopting Sustainable and Environmentally Friendly Energy Solutions with Advanced Computational Techniques

An Agile Approach for Adopting Sustainable Energy Solutions with Advanced Computational Techniques

Multiobjective optimization of perforation design with mechanism learning to increase the stimulated reservoir volume of unconventional reservoirs

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