Solution of the Multi-objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm

Economic load dispatch is a complex and significant problem in power generation. The inclusion of emission with economic operation makes it a Multi-objective economic emission load dispatch (MOEELD) problem. So it is a tough task to resolve a constrained MOEELD problem with antagonistic multiple objectives of emission and cost. Evolutionary Algorithms (EA) have been widely used for solving such complex multi-objective problems. However, the performance of EAs on such problems is dependent on the choice of the operators and their parameters, which becomes a complex issue to solve in itself. The present work is carried out to solve a Multi-objective economic emission load dispatch problem using a Multi-objective adaptive real coded quantum-inspired evolutionary algorithm (MO-ARQIEA) with gratifying all the constraints of unit and system. A repair-based constraint handling and adaptive quantum crossover operator (ACO) are used to satisfy the constraints and preserve the diversity of the suggested approach. The suggested approach is evaluated on the IEEE 30-Bus system consisting of six generating units. These results obtained for different test cases are compared with other reputed and well-known techniques

TRIPLE OBJECTIVE OPTIMIZATION OF CYTOTOXIC POTENCY OF HUMAN CARCINOMA CELL LINES OF A MARINE MACROALGAE USING NON-SORTING GENETIC ALGORITHM–A THEORETICAL STUDY

Objective: The main purpose of our work was to arrive at an acceptable model for optimizing the cytotoxic potency of Ulva fasciata Delile extract on human carcinoma cell lines of which can provide believable indications as compared to experimental results.Methods: The experimental result for cytotoxic potency of a methanolic extract of the Ulva fasciata Delile (MEUF) with a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay against human colon carcinoma (HT-29), human hepatocyte carcinoma (Hep-G2), and human breast carcinoma (MCF-7) cell lines was used to carry out a multi-objective (triple objective) optimization. Thirty non-dominating solutions were considered for analyses of absorbance (y1), % cell survival (y2) and% cell inhibition (y3) data.Results: The model developed using non-dominated sorting genetic algorithm (NSGA) was compared with data obtained experimentally and the results were found to be significant. This method has distinct advantages over other methods which relied heavily on statistical-regression-models, in the sense that it does triple-objective optimization. The resulted in obtaining solutions which were not only significant or believable, but it also corroborated well with experimental results. Thus the solutions obtained during optimization provided the necessary data for generating a successful model.Conclusion: The solutions obtained by NSGA method helped to build an acceptable model for optimizing the cytotoxic potency of Ulva fasciata Delile on human carcinoma cell lines

Reactive evolutionary path planning for autonomous surface vehicles in lake environments.

Autonomous Surface Vehicles (ASVs) have found a lot of promising applications in aquatic environments, i.e., sea, lakes, rivers, etc. They can be used for applications of paramount importance, such as environmental monitoring of water resources, and for bathymetry to study the characteristics of the basing of a lake/sea or for surveillance in patrol missions, among others. These vehicles can be built with smaller dimensions when compared to regular ships since they do not need an on-board crew for operation. However, they do require at least a telemetry control as well as certain intelligence for making decisions and responding to changing scenarios. Water resources are very important in Paraguay since they provide fresh water for its inhabitants and they are crucial for the main economic activities such as agriculture and cattle raising. Furthermore, they are natural borders with the surrounding countries, and consequently the main transportation route for importing/exporting products. In fact, Paraguay is the third country in the world with the largest fleet of barges after USA and China. Thus, maintaining and monitoring the environmental conditions of these resources is key in the development of the country. This work is focused on the maintenance and monitoring of the greatest lake of the country called Ypacarai Lake. In recent years, the quality of its water has been seriously degraded due to the pollution caused by the low control of the dumping of waste thrown into the Lake. Since it is also a national icon, the government of Paraguay has put a lot of effort in recovering water quality of the Lake. As a result, it is monitored periodically but using manual procedures. Therefore, the primary objective of this work is to develop these monitoring tasks autonomously by means of an ASV with a suitable path planning strategy. Path planning is an active research area in robotics. A particular case is the Coverage Path Planning (CPP) problem, where an algorithm should find a path that achieves the best coverage of the target region to be monitored. This work mainly studies the global CPP, which returns a suitable path considering the initial conditions of the environment where the vehicle moves. The first contribution of this thesis is the modeling of the CPP using Hamiltonian Circuits (HCs) and Eulerian Circuits (ECs). Therefore, a graph adapted to the Ypacarai Lake is created by using a network of wireless beacons located at the shore of the lake, so that they can be used as data exchange points between a control center and the ASV, and also as waypoints. Regarding the proposed modeling, HCs and ECs are paths that begin and end at the same point. Therefore, the ASV travels across a given graph that is defined by a set of wireless beacons. The main difference between HC and EC is that a HC is a tour that visits each vertex only once while EC visits each edge only once. Finding optimal HCs or ECs that minimize the total distance traveled by the ASV are very complex problems known as NP-complete. To solve such problems, a meta-heuristic algorithm can be a suitable approach since they provide quasi-optimal solutions in a reasonable time. In this work, a GA (Genetic Algorithm) approach is proposed and tested. First, an evaluation of the performance of the algorithm with different values of its hyper-parameters has been carried out. Second, the proposed approach has been compared to other approaches such as randomized and greedy algorithms. Third, a thorough comparison between the performance of HC and EC based approaches is presented. The simulation results show that EC-based approach outperforms the HC-based approach almost 2% which in terms of the Lake size is about 1.4 km2 or 140 ha (hectares). Therefore, it has been demonstrated that the modeling of the problem as an Eulerian graph provides better results. Furthermore, it has been investigated the relationship between the number of beacons to be visited and the distance traveled by the ASV in the EC-based approach. Findings indicate that there is a quasi-lineal relationship between the number of beacons and the distance traveled. The second contribution of this work is the development of an on-line learning strategy using the same model but considering dynamic contamination events in the Lake. Dynamic events mean the appearance and evolution of an algae bloom, which is a strong indicator of the degradation of the lake. The strategy is divided into two-phases, the initial exploration phase to discover the presence of the algae bloom and next the intensification phase to focus on the region where the contamination event is detected. This intensification effect is achieved by modifying the beacon-based graph, reducing the number of vertices and selecting those that are closer to the region of interest. The simulation results reveal that the proposed strategy detects two events and monitors them, keeping a high level of coverage while minimizing the distance traveled by the ASV. The proposed scheme is a reactive path planning that adapts to the environmental conditions. This scheme makes decisions in an autonomous way and it switches from the exploratory phase to the intensification phase depending on the external conditions, leading to a variable granularity in the monitoring task. Therefore, there is a balance between coverage and the energy consumed by the ASV. The main benefits obtained from the second contribution includes a better monitoring in the quality of water and control of waste dumping, and the possibility to predict the appearance of algae-bloom from the collected environmental data

A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

Bio-inspired Optimization: Algorithm, Analysis and Scope of Application

In the last few years, bio-inspired optimization techniques have been widely adopted in fields such as computer science, mathematics, and biology in order to optimize solutions. Bio inspired optimization problems are usually nonlinear and restricted to multiple nonlinear constraints to tackle the problems of the traditional optimization algorithms, the recent trends tend to apply bio-inspired optimization algorithms which represent a promising approach for solving complex optimization problems. This work comprises state-of-art of ten recent bio-inspired algorithms, gap analysis, and its applications namely; Particle swarm optimization (PSO), Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant Search Algorithm (ESA), Cuckoo Search Optimization Algorithm (CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization (GWO) algorithm. The previous related works collected from Scopus databases are presented. Also, we explore some key issues in optimization and some applications for further research. We also analyze in-depth discussions on the essence of these algorithms and their connections to self-organization and their applications in different areas of research are presented. As a result, the proposed analysis of these algorithms leads to some key problems that have to be addressed in the future

Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem

The multidimensional knapsack problem (MKP) is a generalization of the classical knapsack problem, a problem for allocating a resource by selecting a subset of objects that seek for the highest profit while satisfying the capacity of knapsack constraint. The MKP have many practical applications in different areas and classified as a NP-hard problem. An exact method like branch and bound and dynamic programming can solve the problem, but its time computation increases exponentially with the size of the problem. Whereas some approximation method has been developed to produce a near-optimal solution within reasonable computational times. In this paper a pigeon inspired optimization (PIO) is proposed for solving MKP. PIO is one of the metaheuristic algorithms that is classified in population-based swarm intelligent that is developed based on the behavior of the pigeon to find its home although it had gone far away from it home. In this paper, PIO implementation to solve MKP is applied to two different characteristic cases in total 10 cases. The result of the implementation of the two-best combination of parameter values for 10 cases compared to particle swarm optimization, intelligent water drop algorithm and the genetic algorithm gives satisfactory results

Layout optimisation algorithms and reliability assessment of wind farm for microgrid integration:A comprehensive review

Abstract The paper represents a comprehensive review of the wind farm layout and reliability assessment of the wind farm integrated electrical power system. The authors have done a review on the proliferation of renewable energy which raises the uncertainties in the electrical power system. The uncertainties including wind speed and wake effect are important to deal with when an isolated microgrid is considered. The scenario becomes vigilant when the wind farms are integrated with the main grid. Due to uncertainties, the study of reliability evaluation of a wind integrated power system would become significant to analyse the electrical power system behaviour effectively. So, the paper discusses the layout optimisation methods of wind turbines considering the uncertainty parameters, mainly the wake effect. In this regard, the different wake models and optimisation methods based on a single‐objective and multi‐objective functions are reviewed in detail with the proper comparisons. The paper serves as a better illustration of the competency of these optimisation methods on the optimal wind turbine location on a wind farm. Furthermore, the paper extends the view on the reliability and cost assessment, and reliability improvement techniques of the wind integrated power system. This article provides comprehensive information, yields an attractive and subsequent tool for research requirements for the researchers to design the wind farm layout, and assessed the reliability of a wind integrated power system

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems