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We describe particle swarm inspired evolutionary algorithm (PS-EA), which is a hybridized evolutionary algorithm (EA) combining the concepts of EA and particle swarm theory. PS-EA is developed in aim to extend PSO algorithm to effectively search in multiconstrained solution spaces, due to the constraints rigidly imposed by the PSO equations. To overcome the constraints, PS-EA replaces the PSO equations completely with a self-updating mechanism (SUM), which emulates the workings of the equations. A comparison is performed between PS-EA with genetic algorithm (GA) and PSO and it is found that PS-EA provides an advantage over typical GA and PSO for complex multimodal functions like Rosenbrock, Schwefel and Rastrigrin functions. An application of PS-EA to minimize the classic Fonseca 2-objective functions is also described to illustrate the feasibility of PS-EA as a multiobjective search algorithm.