Let’s understand what is MPSO technique in Operation Research?
MPSO, which stands for “multi-objective particle swarm optimization,” is a metaheuristic optimization algorithm that is used to find multiple optimal solutions to a multi-objective optimization problem.
Multi-objective optimization problems involve finding a set of solutions that are optimal with respect to multiple conflicting objectives.
The basic idea behind MPSO is to use a swarm of particles to search the solution space, where each particle represents a candidate solution.
The swarm moves through the solution space over time, guided by the value of the objective functions and the interactions between particles.
In MPSO,
The particles are updated using a combination of their own best position, the global best position found by the entire swarm, and a randomly generated velocity.
The velocity of each particle is used to determine its movement in the solution space, and it is updated based on the values of the objective functions and the positions of other particles.
The goal of MPSO is to find a set of Pareto-optimal solutions, which are solutions that are not dominated by any other solutions in the set.
These solutions represent the trade-off between the conflicting objectives, and they can be used to support decision-making by providing multiple possible solutions for a given problem.
MPSO is widely used in various fields,
Including engineering, finance, and environmental science, where it can be applied to a variety of multi-objective optimization problems, including portfolio optimization, facility location, and resource allocation.
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