Particle swarm optimization

CHAPTER 4 PSO AND GA TECHNIQUE 4.1 Moth Flame Optimization In the proposed MFO algorithm, I assumed that the candidate solutions are moths and the problem’s variables are the position of moths in the space. Therefore, the moths can fly in 1-D, 2-D, 3-D, or hyper dimensional space with changing their position vectors. Since the MFO algorithm is a population-based algorithm. It should be noted here that moths and flames are both solutions. The difference between them is the way we treat and update them in each iteration. The moths are actual search agents that move around the search space, whereas flames are the best position of moths that obtains so far. In other words, flames can be considered as flags or pins that are dropped by moths…

2.3.2 PARTICLE SWARM OPTIMIZATION (PSO) Particle Swarm Optimization [27] is a population-based stochastic optimization developed by Dr. Ebehart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. In PSO, each single solution is a “bird” (particle) in the search space of food (the best solution). All particles have fitness values evaluated by the fitness function (the cost function for ELD problem), and have velocities that direct the “flying” (or evaluation)…

Ant colony algorithm has three steps: pheromone initialization, building tour, updating pheromone.For the ant colony algorithm, path selection influence a little by the pheromone initialization. In order to get a better result, the pheromone value is always initialized slightly higher than the one released in each iteration.We have taken the initial value of pheromone as τij(0)= m/Cmin where , τij(t) is the residual pheromone on the path between node i and node j at moment t. m is the number of…

Traveling Salesman Problem (TSP) is one of combinatorial optimization problems. X TSP is NP-hard problem which defined as a set of cities and each city should be visited once with minimum tour length. This paper solved this problem using Firefly Algorithm (FA) and k-means clustering by three steps: cluster the nodes, finding optimal path in each cluster and connect the clusters. The first step is to divide all nodes into sub-problems using k-means clustering, the second step is to use FA to find…

grain-machine interaction systems responses where the micro parameters in DEM models are developed using particle-particle friction, particle-geometry friction and the particle density to simulate material behavior (Asaf et al., 2007). The particle size and shape distribution are also considered to be input DEM parameters. In DEM, spherical particles are usually preferred because of the efficiency of contact detection. However, when using this type of particles, the bulk friction of the assembly…

MORAL DILEMMA Ant and Grasshopper Stephanie Gardner Should the ant give food to the grasshopper? Yes and no. Now I say both for a variety of different reasons. I have a feeling that the ant in this situation is not mean enough to just let the grasshopper go hungry. But is the grasshopper allowed to just get off without any punishment for not working hard? This is a classic argument of mercy verses justice. The ant can't show the grasshopper the mercy that he desires and at the same time have…

all the tours. * Temperature is cooled by the predetermined cooling factor in each iteration. * Once the process ends we have the best possible . C. Pseudo Code 1. Choose an initial tour S 2. Choose a temperature = > 0 3. Repeat : a.Choose a new tour S’ b. Let = , where is energy (length) of tour c. If , accept new tour i.e., d. Else if , accept new tour e. Else reject new tour f. Reduce temperature according to cooling factor 4. Until termination conditions are met 1.…

Simulation In this study, optimisation of both parameters together with energy performance assessment of the mechanical ventilated PV façade system are conducted using TRNSYS. The schematic diagram of the components used to simulate the system is provided in Figure 2. The façade is part of a prototypical daylit cellular office building that is represented by Type 56 in Figure 2. This built form is chosen because it accounts for more than 67% of office buildings in England and Wales [25]. The…

It is inserted into the equation simply to give a positive solution at the origin; we are artificially creating a solution: 2x1 + 4x2 - s1 + A1 = 16 2(0) + 4(0) - 0 + A1 = 16 A1 = 16 The artificial variable is somewhat analogous to a booster rocket—its purpose is to get us off the ground; but once we get started, it has no real use and thus is discarded. The artificial solution helps get the simplex process started, but we do not want it to end up in the optimal solution, because it has no real…

Linear programming which is also known as “Linear Optimization” is a way to achieve best outcomes in a Mathematical Model using different linear solutions .Linear Programming is a special case of Mathematical Optimization .Linear programming can be applied to a wide variety of fields of study, and has proved useful in planning, routing, scheduling, assignment, and design, such as in transportation or manufacturing industries. The method of Linear Programming was originally developed by American…