Since its inception in 2014, the Competitive Swarm Optimizer (CSO) has emerged as a
significant advancement in the field of swarm intelligence, particularly in addressing large …

With the development of digital economy technologies, mobile edge computing (MEC) has
emerged as a promising computing paradigm that provides mobile devices with closer edge …

When tackling large-scale multi-objective problems (LSMOPs), the computational budget
could be wasted by traditional offspring generators that explore the search space in a nearly …

Large-scale multi-objective optimization problems (LSMOPs), characterized by a substantial
number of decision variables, pose significant challenges for many existing evolutionary …

This paper proposes an efficient variant of the multiplayer battle game optimizer (MBGO)
named gene-targeting MBGO (GTMBGO). We simplify the original MBGO and introduce a …

Edge computing is an effective complementary technology to cloud computing, allowing end
devices to offload tasks onto edge base stations (BSs) to satisfy the quality of experience of …

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective
optimization problems (LMOPs). However, most existing methods of grouping decision …

The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of
decision variables, is an advanced method for handling high-dimensional decision …

It is agreed that portfolio selection is of great importance for the financial market. Numerous
outstanding exact and heuristic algorithms have been proposed in the past decades …

Large-scale expensive optimization problems (LSEOPs) are characterized by a large
number of dimensions in the decision space and expensive objective functions. The curse of …