Evolutionary algorithms have been recognized to be suitable for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier‎. ‎This paper applies an evolutionary optimization scheme‎, ‎inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategi...

The optimal Waste Load Allocation (WLA) problem, in general, is a mathematical model incorporating a water quality simulator within the framework of multi objective optimization. The goals are defined as to improve the water quality and minimize the total treatment cost of dischargers. The fraction removal levels of the dischargers are the decision variables. This paper explores the capabilitie...

This research offers an improved method for the self-organization of a swarm UAVs based on social learning approach. To start, we use three different colonies and best members i.e., unmanned aerial vehicles (UAVs) randomly placed in colonies. study uses max-min ant colony optimization (MMACO) conjunction with mechanism to plan optimized path individual colony. Hereinafter, multi-agent system (M...

The role of phases and their interplay in molecular vibrational quantum computing with multiple qubits Chirp-driven vibrational distribution in transition metal carbonyl complexes Phys. Robustness of quantum gates operating on the high frequency modes of MnBr(CO) 5 Comment on " Anharmonic properties of the vibrational quantum computer " [J. Monotonic convergent optimal control theory with stric...

many real world problems can be modelled as an optimization problem. evolutionary algorithms are used to solve these problems. ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. these ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. ant colony optim...

water conveyance systems (wcss) are costly infrastructures in terms of materials, construction, maintenance and energy requirements. much attention has been given to the application of optimization methods to minimize the costs associated with such infrastructures. historically, traditional optimization techniques have been used, such as linear and non-linear programming. in this paper, applica...

this paper considers the job scheduling problem in virtual manufacturing cells (vmcs) with the goal of minimizing two objectives namely, makespan and total travelling distance. to solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (nsga-ii) and knowledge-based non-dominated sorting genetic algorithm (kbnsga-ii). the difference between these algor...

Proteins interact with each other in a highly specific manner, and protein interactions play a key role in many cellular processes. Since protein interactions determine the outcome of most cellular processes, so identifying and characterizing Protein– Protein interactions and their networks are essential for understanding the mechanisms of biological processes on a molecular level. This paper e...

This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-partitioning. Ant colony optimization is an optimization method based on behaviour of social insects [27] whereas Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural evolution and its concept of survival of the fittest [19]. Both the methods are stochasti...

Optimization of reservoir parameters is an important issue in petroleum exploration and production. The Ant Colony Optimization(ACO) is a recent approach to solve discrete and continuous optimization problems. In this paper, the Ant Colony Optimization is usedas an intelligent tool to estimate reservoir rock properties. The methodology is illustrated by using a case study on shear wave velocity...