Question

A Lo have multiple disjointed feasible regions as long as it has atleast

Answer 1

Step-by-step explanation:

In constrained optimization problems set in continuous spaces, a feasible search space may consist of many disjoint regions and the global optimal solution might be within any of them. Thus, locating these feasible regions (as many as possible, ideally all of them) is of a great importance. In this chapter, we introduce niching techniques that have been studied in connection with multimodal optimization for locating feasible regions, rather than for finding different local optima. One of the successful niching techniques was based on the particle swarm optimizer (PSO) with a specific topology, called non-overlapping topology, where the swarm was divided into several non -overlapping sub-swarms. Earlier studies have shown that PSO with such non-overlapping topology, with a small number of particles in each sub-swarm, is quite effective in locating different local optima if the number of dimensions is small (up to 8). However, its performance drops rapidly when the number of dimensions grows. First, a new PSO, called mutation linear PSO, MLPSO, is proposed. This algorithm is effective in locating different local optima when the number of dimensions grows. MLPSO is applied to optimization problems with up to 50 dimensions, and its results in locating different local optima are compared with earlier algorithms. Second, we incorporate a constraint handling