UAV mission planning is one of the core problems in the field of UAV applications. Currently,
mission planning needs to simultaneously optimize multiple conflicting objectives and take …

Unmanned aerial vehicles (UAVs) require pre-planned flight paths that are energy-efficient,
safe and smooth across their wide range of application scenarios. In this study, a novel UAV …

In recent years, solving constrained multiobjective optimization problems (CMOPs) by
introducing simple helper problems has become a popular concept. To date, no systematic …

The nature of the real-world problem is multi-modal and multidimensional. This paper
proposes a novel metaheuristic algorithm based on social behaviors of people acquiring …

The objective and constraints of expensive constrained optimization problems (ECOPs) are
often evaluated using simulations with different computational costs. However, the existing …

Distributed optimization algorithms have the advantages of privacy protection and parallel
computing. However, the distributed nature of these algorithms makes the system vulnerable …

Deploying virtual machines poses a significant challenge for cloud data centers, requiring
careful consideration of various objectives such as minimizing energy consumption …

A two-phase constraint-handling technique is integrated into the evolutionary algorithms to
solve constrained optimization problems (called TPDE) in this article. In phase one, denoted …

Teaching–learning-based optimization (TLBO) is a powerful metaheuristic algorithm for
solving complex optimization problems pertaining to the global optimum. Many TLBO …

Many surrogate-assisted evolutionary algorithms (SAEAs) have been shown excellent
search performance in solving expensive constrained optimization problems (ECOPs) with …