A hybrid fine-timed multi-objective memetic algorithm 

 Abstract

 In this paper, we propose a hybrid fine-tuned multiobjective memetic algorithm hybridizing different 
 solution fitness evaluation methods for global exploitation and exploration. To search across all regions 
 in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs 
 a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique 
 is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based 
 local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous 
 areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online 
 optimization conditions based on a function of improvement ratio to obtain better stability and convergence 
 of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack 
 problem (MOKP).