An Evolutionary Algorithmic Framework to Solve Multi-objective Optimization Problems with Variable Length Chromosome Population

In the last decade, Evolutionary Algorithms (EAs) have been widely used to solve optimization problems in the real world. EAs are population-based algorithms, starting the search with initial set of candidates or chromosomes, for the optimal solution of a given optimization problem. Traditional EAs use a population with Fixed Length Chromosomes (FLCs). In FLCs, all the chromosomes will have same length, whereas, in Variable Length Chromosomes (VLCs), a population can have chromosomes of different lengths. This paper proposes to use VLCs in the context of Multi-Objective Differential Evolution (MODE) algorithm. The MODE with VLCs is to solve RFID reader placement problem for the buildings with multiple rooms of different sizes. The type of coverage of RFID readers considered is elliptical. Based on the dimensions of each room, the number of RFID readers required is varied, which warrants the deployment of VLCs. This paper also presents the consequence of VLCs, in solving the RFID reader placement problem using different weight vectors.