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In this paper, hybridization of invasive weed optimization (IWO) and space transformation search (STS) are presented to solve, by applying multiple populations for multiple objectives individually, multiobjective optimization. This whole process is addressed as hybrid cooperative multiobjective optimization IWO (HCMOIWO). We carried out an application to solve system of nonlinear equations. In HCMOIWO, M single objectives are optimized simultaneously using the hybrid IWO with STS and all the nondominated solutions that are extracted from the group of parent weeds and offspring are stored in an archive, A . This archive is used not only to store nondominated solutions, but also to exchange information among subpopulations to explore the new search areas along the Pareto front. To exploit the nondominated solutions, a local search technique is adopted in HCMOIWO. The performance of HCMOIWO is evaluated with different sets of benchmark problems having different characteristics. Empirical results reveal the supremacy of HCMOIWO over state-of-the-art algorithms reported in recent literature. [ABSTRACT FROM AUTHOR]

The populations of industrialized countries will decline over the coming decades. To ensure that we have efficient access networks in the coming age of depopulation, it is important to consider the reorganization of central offices over time. The cost for operating and maintaining central offices can be reduced by efficiently closing central offices. However, dynamic facility location models that have been developed largely focused on the problem of optimally expanding a network over time to meet increasing demand. The practical relevance of the dynamic models is limited because the complexity and the required amount of data are much greater than with static models. This paper proposes a dynamic access network reorganization model for the depopulation age. Although the proposed model is a dynamic model, it is simple because it assumes that the only option will be to close central offices. The model first searches for a set of optimal solutions for each planning period as a capacitated facility location problem. Then, it selects the optimal combination of optimal solutions that can minimize the total number of open offices with the limitation of network reorganization. The validity of the proposed model was evaluated with computer simulations. It was confirmed that although the proposed model is a dynamic model it is simple and practical. [ABSTRACT FROM PUBLISHER]