Sentence examples for compromising solutions from inspiring English sources

Our findings indicate that the proposed algorithm produces compromising solutions of substantial structural diversity and can thus be used for an efficient search of the pharmacologically interesting chemical space as defined by the supplied ligands and constrained by the objectives defined.

In an attempt to find compromising solutions to meet the different (and often conflicting) objectives of the multiple actors involved in the decision making process, flexible methods and tools are needed.

Prakash et al. (2014) proposed a multi-objective simulation optimization (S O) framework with non-dominated sorting genetic algorithm-II (NSGA-II) as the solution technique for flood mitigation in a river-reservoir system with multiple reservoirs and control points for obtaining compromising solutions for the flood mitigation.

The use of granular neighborhoods is one way to improve the run-time of local-search-based metaheuristics for combinatorial optimization problems without compromising solution quality.

Performance comparisons between the LRR, ETP, and FR grids indicate that the LRR method provides substantial savings in execution time and computer memory requirements, without compromising solution accuracy.

Notice that the solution of the above problem is a compromising solution of Problem (1.1) by a suitable.

As is illustrated in Fig. 10, he/she may choose a compromising solution, namely (BP), that represents $499 cost, 56 % GR, and 92 % service level.

A compromising solution which is able to balance the conservatism and optimality through coordinating various DR resources involved can be attained by the proposed robust congestion management model.

In particular, a compromising solution in aspect of not only reducing the conservatism but also suppressing the computational burden is obtained in the special case of m=1.

By using the designers' preference, each design objective is aggregated and multi-objective optimization problem is formulated as finding the best compromising solution between structural control performance and control requirements.

Furthermore, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed to optimize the objective functions, and the most compromising solution (i.e. Re = 1468, C = 0.35, and P∗ = 3.97) was finally selected from the Pareto front attained by the multi-objective optimization.