The large neighborhood search (LNS) framework introduced by Shaw [11] provides a powerful hybridization of Local Search (LS) and Constraint Programming (CP). The idea of LNS is to combine the expressiveness of CP and the efciency of Local Search (LS) without requiring any meta-heuristic. LNS consists in improving a best-so-far solution by iteratively relaxing it and optimizing this solution usi...

Abstract Joint operation of reservoir group is an important method for flood mitigation. Most the existing control methods only focus on a single and object, which cannot take into account coordination complementarity different reservoirs, it difficult to comprehensively consider safety multiple objectives in upstream downstream basin. In this context, paper proposed multi-objective modeling op...

mobile-ad-hoc-networks (manets) are self-configuring networks of mobile nodes, which communicate through wireless links. the infrastructure of a mobile ad hoc network (manet) has no routers for routing, and all nodes must share the same routing protocol to assist each other when transmitting messages. the main issues in manets include the mobility of the network nodes, the scarcity of computati...

Abstract Optimization problems with multiple objectives and many input variables inherit challenges from both large-scale optimization multi-objective optimization. To solve the problems, decomposition transformation methods are frequently used. In this study, an improved control variable analysis is proposed based on dominance diversity in Pareto Further, method used a cooperative coevolution ...

this paper presents a new mathematical model for a bi-objective job shop scheduling problem with sequence-dependent setup times that minimizes the weighted mean completion time and the weighted mean tardiness time. for solving this multi-objective model, we develop a fuzzy multi-objective linear programming (fmolp) model. in this problem, a proposed fmolp method is applied with respect to the o...

Abstract Sparse large-scale multi-objective optimization problems (LSMOPs) widely exist in real-world applications, which have the properties of involving a large number decision variables and sparse Pareto optimal solutions, i.e., most these solutions are zero. In recent years, LSMOPs attracted increasing attentions evolutionary computation community. However, all recently tailored algorithms ...