Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing and have received much attention in recent decades. This study seeks to solve MO-OPs through a problem-decomposition framework, that is, an MO-OP is decomposed into knapsack problem (MOKP) traveling salesman (TSP). The MOKP TSP then solved by evolutionary algorithm (MOEA) deep reinforcement learning (DRL) metho...

Many-objective optimization has been gaining increasing attention in the evolutionary multiobjective optimization community, and various approaches have been developed to solve many-objective problems in recent years. However, the existing empirically comparative studies are often restricted to only a few approaches on a handful of test problems. This paper provides a systematic comparison of e...

In this article, our interest is focused on the automatic learning of Boolean queries in information retrieval systems (IRSs) by means of multi-objective evolutionary algorithms considering the classic performance criteria, precision and recall. We present a comparative study of four well-known, general-purpose, multi-objective evolutionary algorithms to learn Boolean queries in IRSs. These evo...

The core element in solving constrained multi-objective problems (CMOPs) with evolutionary algorithms is simultaneously balancing objective optimization and constraint satisfaction. Maintaining this balance becomes more challenging for existing when dealing complex CMOPs, as various feasible regions often result CMOPs very different characteristics. To address issue, we propose a flexible two-s...

In multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to same problem. It lead poor results. Therefore, search should non-dominated that are also in objective space. this paper, we use two populations search. The first population maintains non-dominance and diversity space provides ...