Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Energies, Journal Year: 2024, Volume and Issue: 17(7), P. 1760 - 1760

Published: April 7, 2024

Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays significant role in reducing energy consumption and environmental pollution. The development goals microgrids not only aim to meet the basic demands electricity supply but also enhance economic benefits protection. In this regard, multi-objective scheduling model for grid-connected mode is proposed, which comprehensively considers operational costs protection microgrid systems. This incorporates improvements traditional particle swarm (PSO) algorithm by considering inertia factors adaptive mutation, it utilizes improved solve model. Simulation results demonstrate that can effectively reduce users pollution, promoting optimized operation verifying superior performance PSO algorithm. After algorithmic improvements, optimal total cost achieved was CNY 836.23, representing decrease from pre-improvement value 850.

Language: Английский

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Constrained multi-objective optimization problems: Methodologies, algorithms and applications

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 299, P. 111998 - 111998

Published: May 29, 2024

Constrained multi-objective optimization problems (CMOPs) are widespread in practical applications such as engineering design, resource allocation, and scheduling optimization. It is high challenging for CMOPs to balance the convergence diversity due conflicting objectives complex constraints. Researchers have developed a variety of constrained algorithms (CMOAs) find set optimal solutions, including evolutionary machine learning-based methods. These exhibit distinct advantages solving different categories CMOPs. Recently, (CMOEAs) emerged popular approach, with several literature reviews available. However, there lack comprehensive-view survey on methods CMOAs, limiting researchers track cutting-edge investigations this research direction. Therefore, paper latest handling A new classification method proposed divide literature, containing classical mathematical methods, learning Subsequently, it modeling context applications. Lastly, gives potential directions respect This able provide guidance inspiration scholars studying

Language: Английский

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Multi-objective simulated annealing algorithm for robotic mixed-model two-sided assembly line balancing with setup times and multiple constraints

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 156, P. 111507 - 111507

Published: March 16, 2024

Language: Английский

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Handling constrained many-objective optimization problems via determinantal point processes

Information Sciences, Journal Year: 2023, Volume and Issue: 643, P. 119260 - 119260

Published: June 2, 2023

Language: Английский

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A Two-Phase Kriging-Assisted Evolutionary Algorithm for Expensive Constrained Multiobjective Optimization Problems

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2024, Volume and Issue: 54(8), P. 4579 - 4591

Published: April 30, 2024

This article devises a two-phase Kriging-assisted evolutionary algorithm (named TEA) to tackle expensive constrained multiobjective optimization problems (CMOPs). In the first phase, only objectives are considered, which can help population cross infeasible obstacles and evolve toward unconstrained Pareto front. Since front is in of feasible region objective space, phase find some solutions during evolution. second both constraints considered. this article, we also propose two transition conditions judge whether search should be switched from by making use candidates evaluated original phase. These aim at maintaining high-quality when ends, able motivate converge with good diversity Furthermore, phases, design new dominance relationship (called PDPD) incorporating probability distribution information derived Kriging models. PDPD further generalized handle CMOPs, Constrained (CPDPD), provides high credibility for comparison between individuals respect constraints. Finally, three benchmark test suites real-world application confirm superiority TEA.

Language: Английский

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Evolutionary constrained multi-objective optimization: a review

Vicinagearth., Journal Year: 2024, Volume and Issue: 1(1)

Published: July 4, 2024

Abstract Solving constrained multi-objective optimization problems (CMOPs) is challenging due to the simultaneous consideration of multiple conflicting objectives that need be optimized and complex constraints satisfied. To address this class problems, a large number evolutionary algorithms (CMOEAs) have been designed. This paper presents comprehensive review state-of-the-art for solving CMOPs. First, background knowledge concepts are presented. Then, some classic constraint handling technologies (CHTs) introduced, advantages limitations each CHT discussed. Subsequently, based on mechanisms used by these algorithms, CMOEAs classified into six categories, which explained in detail. Following that, benchmark test evaluate algorithm’s performance reviewed. Moreover, experimental comparison analysis different types carried out with characteristics. Finally, challenges future research directions

Language: Английский

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Lightweight design optimization of two-layer corrugated cored sandwich panel under blast loading using surrogate-assisted different evolution for mixed-integer variables

Engineering Structures, Journal Year: 2024, Volume and Issue: 321, P. 118963 - 118963

Published: Sept. 14, 2024

Language: Английский

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Design and development of coating for metallic bipolar plates in proton exchange membrane fuel cell (PEMFC): A review

Materials & Design, Journal Year: 2024, Volume and Issue: unknown, P. 113338 - 113338

Published: Sept. 1, 2024

Language: Английский

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Surrogate-assisted global and distributed local collaborative optimization algorithm for expensive constrained optimization problems

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 11, 2025

This paper presents a surrogate-assisted global and distributed local collaborative optimization (SGDLCO) algorithm for expensive constrained problems where two surrogate phases are executed collaboratively at each generation. As the complexity of cost solutions increase in practical applications, how to efficiently solve with limited computational resources has become an important area research. Traditional algorithms often struggle balance efficiency searches, especially when dealing high-dimensional complex constraint conditions. For evolution phase, candidate set is generated through classification mutation operations alleviate pre-screening pressure model. central region exploration designed achieve intensively search promising areas which located by affinity propagation clustering mathematical modeling. More importantly, three-layer adaptive selection strategy feasibility, diversity convergence balanced effectively identify sets. Therefore, SGDLCO balances during whole process. Experimental studies on five classical test suites demonstrate that provides excellent performance solving problems.

Language: Английский

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Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization

Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(2)

Published: Jan. 15, 2025

Language: Английский

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