Conventional clustering algorithms do not recognize patterns and structures with
contradicting objectives in large, distributed datasets. Distributed clustering leverages rapid …

We study the multi-objective minimum weight base problem, an abstraction of classical NP-
hard combinatorial problems such as the multi-objective minimum spanning tree problem …

Abstract Nature-Based Solutions can be considered one of the best answers to the various
consequences and problems caused by climate change, poor urbanisation and population …

The optimization techniques usually work with the maximization or minimization of the
problem to obtain the local loci or cumulative global loci. Two-dimensional bio-inspired …

Abstract Generative Adversarial Networks (GANs) are an adversarial approach to generative
modeling using deep learning methods, which have become one of the most relevant topics …

Multi-path and multi-hop routing are multi-objective optimization problems involving multiple
constraints that need to be addressed in the current scenario in wireless sensor networks …

The flying foxes optimization (FFO) algorithm stimulated by the strategy used by flying foxes
for subsistence in heat wave environments has shown good performance in the single …

Given the costs to implement whole Pareto optimal solutions, users often prefer solutions of
interest, like knee points, which represent naturally preferred solutions without a specific …

Aerodynamic shape optimization is frequently complicated and challenging due to the
involvement of multiple objectives, large-scale decision variables, and expensive cost …

Productively reducing the time required to cut numerous through holes in automotive
workpieces is crucial for enhancing parts manufacturing in the 3D laser cutting process …