Dynamic multi-objective optimisation techniques address problems in which several conflicting objectives evolve over time. These methods are critical for applications ranging from smart systems ...

Keywords: multi-objective optimization, sparse Gaussian process, surrogate model, adaptive grid multi-objective particle swarm optimization algorithm, wind power engineering Citation: Chen Y, Wang L ...

Multi-objective optimization techniques, such as multi-objective particle swarm optimization (MOPSO) and nondominated sorting genetic algorithms (NSGA), have proven highly effective in addressing the ...

One of the primary challenges in wind energy development is optimizing the spatial layout of wind turbines within a given site. Poor turbine placement can lead to reduced energy capture due to wake ...

Several approaches have tackled automated architecture exploration using multi-objective optimization techniques. Although these approaches are useful to assist architects when many alternative ...

First, the proposed algorithm employs two distinct multi-objective optimization models, whereas current methods optimize for a single multi-objective model throughout the entire search process. Second ...

This paper addresses the Flexible Job Shop Scheduling Problem (FJSP) in manufacturing by proposing an enhanced genetic algorithm based on multi-objective optimization. As an NP-hard problem, FJSP ...

Dynamic Multi-Objective Optimization Techniques Publication Trend. The graph below shows the total number of publications each year in Dynamic Multi-Objective Optimization Techniques.