A new method based on discrete particle swarm optimization (dPSO) algorithm is proposed to solve the branch-cut phase unwrapping problem of MRI data. In this method, the optimal order of matching the positive residues with the negative residues is first identified by the dPSO algorithm, then the branch cuts are placed to join each pair of the opposite polarity residues, and in the last step pha...

Sub-dimension particle swarm optimization(s-dPSO) is proposed based on basic particle swarm optimization (bPSO). Each dimension of particle in s-dPSO is updated in turn. The dimensions with poor diversity would be mutated that is initialized again to improve the diversity of population and get global optimal solution when the algorithm is in the local convergence. Most Benchmark function get go...

In this paper we present a new Discrete Particle Swarm Optimization (DPSO) approach to face the NP-hard single machine total weighted tardiness scheduling problem in presence of sequence-dependent setup times. Differently from previous approaches the proposed DPSO uses a discrete model both for particle position and velocity and a coherent sequence metric. We tested the proposed DPSO mainly ove...

Discrete particle swarm optimization (DPSO) is a powerful stochastic evolutionary algorithm that is used to solve the large-scale, discrete and nonlinear optimization problems. However, it has been observed that standard DPSO algorithm has premature convergence when solving a complex optimization problem like transmission expansion planning (TEP). To resolve this problem an advanced discrete pa...

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the well-known Particle Swarm Optimization (PSO) using natural selection, or survival-of-the-fittest, to enhance the ability to escape from local optima. In this paper, it is explored the effectiveness of using a modified version of both PSO and DPSO, respectively named as R-PSO and R-DPSO, on groups of s...

This paper proposes a discrete particle swarm optimization (DPSO) to solve the multiple destination routing (MDR) problems. The problem has been proven to be NP-complete and the traditional heuristics (e.g., the SPH, DNH and ADH) are inefficient in solving it. The particle swarm optimization (PSO) is an efficient global search algorithm and is promising in dealing with complex problems. This pa...

One of the most well-known bio-inspired algorithms used in optimization problems is the particle swarm optimization (PSO), which basically consists on a machinelearning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. The Darwinian particle swarm opt...

The lines adequacy of the network is necessary to provide load demands when the network is expanding, because its lack caused by load interruption. In this paper, in order to take the transmission lines condition after expansion into account from the line loading view points, the adequacy of the transmission network is considered for the solution of the STNEP problem. To obtain the optimal netw...

Finding an optimal schedule of tasks for an application in distributed environment is critical in general. Task assignment is an extremely NP complete problem. This type of problem can be resolved by heuristic algorithms efficiently because the traditional methods such as dynamic programming and the back tracking need more time for solving this NP complete problem. Particle Swarm Optimization (...

Abstract To promote the development and application of “blockchain+supply chain finance” in agricultural logistics industry, this paper uses ECC algorithm blockchain model as basis implements an asymmetric encryption based on elliptic curve mathematical theory to optimize algorithm. Based model, Combining dynamic variational particle swarm optimization AdaBoost integration algorithm, DPSO-SVM i...