An Efficient Multi-Objective Optimization Method for Use in the Design of Marine Protected Area Networks
An Efficient Multi-Objective Optimization Method for Use in the Design of Marine Protected Area Networks
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DOI：
10.3389/fmars.2019.00017
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发表时间：
2019-02-05
影响因子：
3.7
通讯作者：
Roberts, J. Murray
中科院分区：
文献类型：
--
作者：
Fox, Alan D.;Corne, David W.;Roberts, J. Murray
提出了一种基于连通度的海洋保护区网络多目标优化设计方法。多目标网络优化突出了以前未报告的步骤变化的最佳子网的结构，以保护相关的成本或效益函数的最小变化。这就强调了对海洋空间规划进行全面、无约束、多目标优化的必要性。对于站点网络，检查受保护和未受保护站点的所有可能组合的蛮力方法对于除了最小网络之外的所有网络都是不切实际的，因为可能网络的数量增长为2（m），其中m是网络内的站点数量。一种基于马尔可夫链蒙特卡罗方法的元启发式方法，该方法在给定两个单独的目标函数的情况下，例如针对网络质量或有效性、种群持久性或保护成本，搜索帕累托最优网络（或其良好近似）的集合。优化和搜索方法与目标函数的选择无关，并且可以很容易地扩展到两个以上的函数。一系列网络配置下的方法的速度，精度和收敛性进行了测试与模型网络的基础上扩展的随机几何图形。检查两个现实世界的海洋网络，一个指定为保护石珊瑚Lophelia pertusa，其他一个假设的人造网络的石油和天然气设施，以保护硬基质生态系统，证明了该方法的力量，在寻找多目标的最佳解决方案的网络多达100个网站。结果使用网络平均最短路径作为人口弹性和基因流在网络中的代理支持使用的保护策略的基础上高度连接的集群的网站。
An efficient connectivity-based method for multi-objective optimization applicable to the design of marine protected area networks is described. Multi-objective network optimization highlighted previously unreported step changes in the structure of optimal subnetworks for protection associated with minimal changes in cost or benefit functions. This emphasizes the desirability of performing a full, unconstrained, multi-objective optimization for marine spatial planning. Brute force methods, examining all possible combinations of protected and unprotected sites for a network of sites, are impractical for all but the smallest networks as the number of possible networks grows as 2(m), where m is the number of sites within the network. A metaheuristic method based around Markov Chain Monte Carlo methods is described which searches for the set of Pareto optimal networks (or a good approximation thereto) given two separate objective functions, for example for network quality or effectiveness, population persistence, or cost of protection. The optimization and search methods are independent of the choice of objective functions and can be easily extended to more than two functions. The speed, accuracy and convergence of the method under a range of network configurations are tested with model networks based on an extension of random geometric graphs. Examination of two real-world marine networks, one designated for the protection of the stony coral Lophelia pertusa, the other a hypothetical man-made network of oil and gas installations to protect hard substrate ecosystems, demonstrates the power of the method in finding multi-objective optimal solutions for networks of up to 100 sites. Results using network average shortest path as a proxy for population resilience and gene flow within the network supports the use of a conservation strategy based around highly connected clusters of sites.