What is multi objective particle swarm optimization?

What is multi objective particle swarm optimization?

In this article, a novel multi-objective particle swarm optimization (PSO) algorithm is proposed based on Gaussian mutation and an improved learning strategy. The approach adopts a Gaussian mutation strategy to improve the uniformity of external archives and current populations.

How can you optimize particle swarm?

Particle Swarm Optimization Algorithm

  1. Create a ‘population’ of agents (particles) which is uniformly distributed over X.
  2. Evaluate each particle’s position considering the objective function( say the below function).
  3. If a particle’s present position is better than its previous best position, update it.

What are the applications of particle swarm optimization?

(1)Call the decoding function; calculate the fitness value of the particle swarm. (2)Update the individual optimal solution and the global optimal solution .(3)Update the speed vector, by using (22)-(23).(4)Update the speed vector, by using (24)-(25).

What are the 2 main equations involved in particle swarm Optimisation?

After finding the two best values, the position and velocity of the particles are updated by the following two equations: v i k = w v i k + c 1 r 1 ( pbest i k − x i k ) + c 2 r 2 ( gbest k − x i k ) x i k + 1 = x i k + v i k + 1 where v i k is the velocity of the th particle at the th iteration, and x i k is the …

What is binary particle swarm optimization?

Particle swarm optimization (PSO) is a heuristic optimization algorithm generally applied to continuous domains. Binary PSO is a form of PSO applied to binary domains but uses the concepts of velocity and momentum from continuous PSO, which leads to its limited performance.

What is PSO swarm size?

The swarm size may be considered the most “basic” control parameter of PSO, as it simply defines the number of individuals in the swarm, and hence its setting can hardly be avoided. It is also one of the most difficult parameters to settle on most metaheuristics [17].

How is particle swarm optimization different from genetic algorithms?

The main difference between the PSO approach compared to EC and GA is that PSO does not have genetic operators such as crossover and mutation. Particles update themselves with the internal velocity; they also have a memory important to the algorithm.

Why PSO is better than other optimization techniques?

Particle Swarm Optimization (PSO) was developed by Kennedy and Eberhart in the mid 1990s [2]. PSO has been used increasingly due to its several advantages like robustness, efficiency and simplicity. When compared with other stochastic algorithms it has been found that PSO requires less computational effort [3] [4].

What is Firefly optimization?

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research.

What is optimization and different optimization techniques?

An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till an optimum or a satisfactory solution is found. There are two distinct types of optimization algorithms widely used today. (a) Deterministic Algorithms. They use specific rules for moving one solution to other.

What is objective function in optimization?

An objective function expresses the main aim of the model which is either to be minimized or maximized. – A set of unknowns or variables which control the value of the objective function. – A set of constraints that allow the unknowns to take on certain values but exclude others.

Can a dynamic neighborhood algorithm solve multimodal optimization problem in particle swarm?

In view of this, we propose a particle swarm optimization algorithm based on dynamic neighborhood to solve the multimodal optimization problem. In this paper, a dynamic ε -neighborhood selection mechanism is first defined to balance the exploration and exploitation of the algorithm.

What is multi-objective particle swarm optimization?

Multi-objective particle swarm optimization (MOPSO) [14, 15] has individual and global optima in the optimization process, and the optimal solution is obtained by iterative optimization along this direction. The specific process is as Fig 5. ( 1 ) Parameter setting, initialization of particle population, calculation of fitness value, and speed.

Can Pareto dominance be incorporated into particle swarm optimization?

This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions.

Can We extend PSO to solve multiobjective optimization problems?

Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm.