Abstract: In multimodal optimization problems the main goal is to find as many global optima as possible by using a single search process. This type of optimization tasks emerges in many real-world ...

Abstract: Multifactorial evolutionary algorithm is used to deal with multifactorial optimization problem which simultaneously optimizes multiple tasks. In this paper, we introduce particle swarm ...

Humans have the ability to identify recurring patterns in diverse situations encountered over a lifetime, constantly understanding relationships between tasks and efficiently solving them through ...

Different from conventional single-task optimization, the recently proposed multitasking optimization (MTO) simultaneously deals with multiple optimization tasks with different types of decision ...

To progress the fields of tissue engineering (TE) and regenerative medicine, development of quantitative methods for non-invasive three dimensional characterization of engineered constructs (i.e.

Present study aims the optimisation and validation of the extraction procedures for the determination of poly- cyclic aromatic hydrocarbons (PAHs) in sediment samples. As analytical techniques, gas ...

See /GLS/README.md for detailed documentation of this innovation. Population size: 200 Maximum generations: 300 Random mating probability (RMP): 0.4 Mutation rate: 0. ...

Evolutionary computation (EC) has gained increasing popularity in dealing with permutation-based combinatorial optimization problems (PCOPs). Traditionally, EC focuses on solving a single optimization ...