Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch With Enhanced Power Demand and Valve Point Loading

Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss

The Gradient Free Directed Search Method as Local Search within Multi-objective Evolutionary Algorithms

Recently, the Directed Search Method has been proposed as a point-wise iterative search procedure that allows to steer the search, in any direction given in objective space, of a multi-objective optimization problem. While the original version requires the objectives’ gradients, we consider here a possible modification that allows to realize the method without gradient information. This makes the novel algorithm in particular interesting for hybridization with set oriented search procedures, such as multi-objective evolutionary algorithms. In this paper, we propose the DDS, a gradient free Directed Search method, and make a first attempt to demonstrate its benefit, as a local search procedure within a memetic strategy, by integrating the DDS into the well-known algorithmMOEA/D. Numerical results on some benchmark models indicate the advantage of the resulting hybrid

DC-DistADMM: ADMM Algorithm for Contrained Distributed Optimization over Directed Graphs

We present a distributed algorithm to solve a multi-agent optimization problem, where the global objective function is the sum $n$ convex objective functions. Our focus is on constrained problems where the agents' estimates are restricted to be in different convex sets. The interconnection topology among the $n$ agents has directed links and each agent $i$ can only communicate with agents in its neighborhood determined by a directed graph. In this article, we propose an algorithm called \underline{D}irected \underline{C}onstrained-\underline{Dist}ributed \underline{A}lternating \underline{D}irection \underline{M}ethod of \underline{M}ultipliers (DC-DistADMM) to solve the above multi-agent convex optimization problem. During every iteration of the DC-DistADMM algorithm, each agent solves a local convex optimization problem and utilizes a finite-time "approximate" consensus protocol to update its local estimate of the optimal solution. To the best of our knowledge the proposed algorithm is the first ADMM based algorithm to solve distributed multi-agent optimization problems in directed interconnection topologies with convergence guarantees. We show that in case of individual functions being convex and not-necessarily differentiable the proposed DC-DistADMM algorithm converges at a rate of $O(1/k)$, where $k$ is the iteration counter. We further establish a linear rate of convergence for the DC-DistADMM algorithm when the global objective function is strongly convex and smooth. We numerically evaluate our proposed algorithm by solving a constrained distributed $\ell_1$-regularized logistic regression problem. Additionally, we provide a numerical comparison of the proposed DC-DistADMM algorithm with the other state-of-the-art algorithms in solving a distributed least squares problem to show the efficacy of the DC-DistADMM algorithm over the existing methods in the literature.Comment: 17 pages, 8 Figures, includes an appendi