In the design of multiobjective particle swarm optimization (MOPSO) algorithms, swarm
leaders, ie, the personal best (pbest) and global best (gbest), are expected to guide the …

Feature selection (FS), which aims to minimize the classification error and the number of
selected features, can essentially be modeled as a multiobjective optimization problem. To …

Many real-world optimization problems are inherently constrained, presenting significant
challenges to the application of evolutionary algorithms. Successfully managing these …

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 …

With the development of information technology, large-scale optimization problem is the
core issue in various applications. Due to the high dimension characteristics, large-scale …

Crowdsourcing logistics has developed rapidly in recent years, in which not only the fleet of
logistics companies but also occasional couriers participate in the distribution process. The …

As a crucial member of the evolutionary computation (EC), ant colony optimization (ACO)
converges to the optimal result through a continuous process of iterative optimization …

Evolutionary Computation (EC) is a class of algorithms derived from the simulation of natural
phenomena, which faces the challenges of long running times and high computing loads …

Multi-agent pickup and delivery (MAPD) holds great significance in practical logistics and
distribution systems. This paper models the problem as a multiobjective problem, wherein a …

With the great development of economic globalization and the logistics industry, various
scenarios in daily life require multiple visits to certain targets derived from the multiple …