Due to the computational intractability, two recently developed solution methods, based on swarm intelligence technique, are developed for problem solution, namely, particle swarm optimization (PSO) and bacterial colony optimization (BCO).
A local search algorithm has been developed for problem solution for large-scale networks.
Quite recently some efficient methods with relaxed proximal matrix [21 24] have been developed for Problem (1).
In this paper, new complex maps are developed for problems containing a single near-boundary critical point.
The conventional tools of decision-making in water resources infrastructure planning have been developed for problems with well-characterized uncertainties and are ill-suited for problems involving climate nonstationarity.
A mixed-integer linear programming model is developed for the problem, and an implicit enumeration and a tabu search heuristic are tested with a suite of problem examples.
Moreover, a novel graph representation is developed for the problem.
A genetic algorithm-based heuristic is developed for the problem.
An efficient simulation budget allocation procedure, called EOC-m, is developed for this problem.
An adaptive large neighbourhood search metaheuristic is developed for the problem.
