  Now-a-days, it is important to find out solutions of Multi-Objective
Optimization Problems (MOPs). Evolutionary Strategy helps to solve such real
world problems efficiently and quickly. But sequential Evolutionary Algorithms
(EAs) require an enormous computation power to solve such problems and it takes
much time to solve large problems. To enhance the performance for solving this
type of problems, this paper presents a new Distributed Novel Evolutionary
Strategy Algorithm (DNESA) for Multi-Objective Optimization. The proposed DNESA
applies the divide-and-conquer approach to decompose population into smaller
sub-population and involves multiple solutions in the form of cooperative
sub-populations. In DNESA, the server distributes the total computation load to
all associate clients and simulation results show that the time for solving
large problems is much less than sequential EAs. Also DNESA shows better
performance in convergence test when compared with other three well-known EAs.
