ABSTRACT Optimal Phasor Measurement Unit (PMU) Placement (OPP) aims to reduce equipment costs while maintaining electrical systems observability. PMUs synchronized phasor data provide electrical systems information with high quality and frequency enabling the implementation of faster than real-time state estimators. We applied Chemical Reaction Optimization (CRO) method at OPP problem, testing it successfully at IEEE power system databases (14, 30, 57 and 118 bars) through two distinct models. The relationships between CRO parameters and the occurrence of elementary reactions was exploited, achieving better results through specific reactions. Due to problems similarity and larger scales, the software was adapted to Beasley OR-Library Set Covering (SC) problems. In the process to achieve GAPs smaller than 10% for some files, we tried out dedicated local searches, disturbance recurrence limits and stop condition changes. However, we suggest continuing to evaluate CRO method adherence to SC problem using different data structures to decrease computational times.

ABSTRACT Chemical Reaction Optimization (CRO) is a metaheuristic for solving optimization problems, which mimics the interactions between molecules in a chemical reaction with the purpose of achieving a stable, low-energy state. In the present work, we utilize the CRO metaheuristic to solve, in an efficient manner, the capacitated p-median problem, in order to locate service stations. Results from solving small to medium-sized problems available in the literature, with up to 724 notes and 200 medians, are compared to their optimal or best-known values. Results show that CRO results are comparable, in terms of accuracy and execution time, to many existing successfulmetaheuristics, as well as exact and hybrid methods, having exceeding those in some cases.

ABSTRACT This paper proposes a hybrid heuristic algorithm, based on the metaheuristics Greedy Randomized Adaptive Search Procedure, Iterated Local Search and Variable Neighborhood Descent, to solve the Clustered Traveling Salesman Problem (CTSP). Hybrid Heuristic algorithm uses several variable neighborhood structures combining the intensification (using local search operators) and diversification (constructive heuristic and perturbation routine). In the CTSP, the vertices are partitioned into clusters and all vertices of each cluster have to be visited contiguously. The CTSP is ����-hard since it includes the well-known Traveling Salesman Problem (TSP) as a special case. Our hybrid heuristic is compared with three heuristics from the literature and an exact method. Computational experiments are reported for different classes of instances. Experimental results show that the proposed hybrid heuristic obtains competitive results within reasonable computational time.