Multi-point search is based on a pool of points. This pool can be managed via parameters.
One search technique available in CP Optimizer is a multi-point search algorithm. This algorithm is based on a pool of search points. A search point is a collection of decision variable assignments that may lead to either feasible or partial solutions (a partial solution has some variables which are still not fixed). The multi-point method starts with an initial pool of search points whose candidate assignments are generated with constructive search. It then produces new search points by learning new variable assignments from search points maintained in the pool. On an optimization problem, the multi-point search method is able to learn from partial solutions in order to produce feasible solutions.
Multi-point search produces solutions by first performing variable assignments proposed by each of the generated search points. It then attempts to complete the solution by invoking a tree-search based completion procedure. If no feasible solution can be produced, a solution with a maximal number of instantiated decision variable is retained.