The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-
optima closer to the true one as soon as possible once a new environment occurs. Previous …

Multimodal multi-objective optimization (MMO) can offer more elegant solutions and provide
diverse decisions to decision-makers in real world optimization problems. Many multimodal …

In this study, an adaptive multi-objective optimization artificial immune algorithm is
presented for reactive power optimization. In the proposed algorithm, a non-inferior solution …

To meet the expectation of multi-modal distributions from customers for last-mile delivery, a
sustainable two-echelon E-Grocery delivery system is studied, which is motivated by the …

Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune
system model. Due to its characteristics of antibody clonal selection, automatic antigen …

Feature selection (FS) is a significant data preprocessing technology in many fields. FS
generally has two primary conflicting goals: 1) reducing features number and 2) improving …

Traditional multiobjective immune algorithms (MOIAs) widely use the domination
relationship and crowding distance metric to run the cloning operator, which places more …

The controlling parameter tuning of the hydraulic turbine governing system (HTGS) is always
deduced under single operating condition and is not suitable for the changeable operating …

Face recognition is a challenging task due to high variations of pose, expression, ageing,
and illumination. As an effective approach to face recognition, feature learning can be …

Inspired by human brains' ability to solve multiple tasks simultaneously, evolutionary
multitasking is proposed to improve the overall efficiency of optimizing multiple tasks …