International Journal of Swarm Intelligence (6 papers in press)
A Survey of Swarm-Inspired Metaheuristics in P2P Systems: Some Theoretical Considerations and Hybrid Forms
by Vesna Sesum-Cavic
Abstract: The growing complexity of nowadays distributed systems influences the application of nature-inspired mechanisms as efficient problem-solving methods. They are important and inevitable for the optimization and robustness of distributed systems, where autonomous agents interact without central control. Especially in the P2P systems, swarm-inspired techniques provide incentives and encourage cooperative behavior between the peers. Many open problems in the P2P systems and cloud computing are characterized by huge and unforeseen dynamics, and number of unpredictable dependencies on participating components. Therefore, there is a demand on self-organizing approaches. Swarm intelligence possesses distributive and autonomous properties, represents a self-organizing biological system and swarm-inspired algorithms play an important role in the P2P systems and cloud computing. This survey paper presents an overview of swarm-inspired algorithms used in P2P systems and cloud computing, describes their underlying biological behaviors, their concept, working, and their main features. Further, the main intention of this paper is to give an overview of the theoretical background of such swarm-inspired metaheuristics in terms of asymptotical behavior, convergence, etc. as well as a thorough overview of the existing hybrid forms (swarm-inspired metaheuristic with another swarm-inspired metaheuristic). In the scope of this survey paper, a new classification of swarm metaheuristics is proposed.
Keywords: swarm-inspired metaheuristic; P2P systems; cloud computing; asymptotic behavior; convergence; hybrid forms.
Special Issue on: Advanced Nature-Inspired Optimisation Techniques for Engineering Applications
Modelling of nature inspired modified Fourier elimination technique for quadratic optimization.
by Sanjay Jain, Adarsh Mangal, Sangeeta .
Abstract: In many decision problems, object occurs in nonlinear nature and expressed as in product form and corresponding optimization problems are quadratic programs of product of two linear functions type. A new technique has been developed to find the solution of quadratic programming problem (QPP) by modelling of modified Fourier elimination technique of inequalities and concept of bounds. The technique is quite useful because the calculations are simple and takes least time then earlier existing methods. The technique has been illustrated by a numerical example also.
Keywords: quadratic programming problem; nature inspired elimination technique; inequalities; nonlinear objective function; constraints.
Application and Development of Improved Meta-Heuristic for making Profitable Bidding Strategy in a Day-Ahead Energy Market under Step-Wise Bidding Scenario
by Pooja Jain, Akash Saxena, Rajesh Kumar
Abstract: An unprecedented concept of restructuring has shaken the structure of electricity industry during the past decades. To sell the produced energy,many generating companies are now forced to prepare and submit daily offers to an electricity market under uncertainty in bid prices submitted by their competitors. To calculate the bid prices optimally and for maximizing the profit of generating company, this paper presents an optimal bidding strategy for a generating company. The company is operating in a day ahead market for single and multi-hourly uniform price multi-unit auctions.In this work, a hybrid model of Whale Optimization Algorithm(WOA) and Sine Cosine Algorithm (SCA), i.e., HWOASCA is proposed to give monte-carlo simulation based solution for strategic bidding problem. By using position update equations of SCA, the feeding characteristics of the whales are improved. First HWOASCA algorithm is validated on 22 standard benchmark test functions then it is applied to bidding problem of 7 GENCO's participating in a dynamically changing electricity market. By suggested approach, the optimal solution for Market Clearing Price (MCP), load dispatch and bid cost under five different capacity and price blocks are calculated. After meaningful comparison from other meta-heuristic techniques, it is observed that the profit obtained by the proposed approach is significantly higher for single-hourly and multi-hourly trading trends.The mathematical and experimental results confirm the supremacy of newly proposed hybrid version of parent algorithms which is highly useful for framing the bidding strategies for a generation company
Keywords: Strategic Bidding; HWOASCA; WOA; SCA ; MCP.
Teaching Learning based Optimization Algorithm: A Survey
by Ruchi Mishra, Nirmala Sharma, Harish Sharma
Abstract: In recent years swarm intelligence (SI) based techniquesrnhave proven their importance for finding the solution ofrnglobal optimization problems. In SI based algorithms agents actrnin a group and learn from each other for food foraging survivingrnetc. Teaching-learning based optimization algorithm (TLBOA) isrnan efficient approach of dealing with linear, nonlinear and multidimensionalrnoptimization problems established by Dr. R.VenkatarnRao in 2011. Since its inception, a lot of research has been carriedrnout to make TLBOA more proficient and to apply it to differentrntypes of optimization problems. This paper presents a review ofrnTLBOA developments, applications, comparative-performance,rnand future research perspectives.
Keywords: Teaching Learning Based Optimization; Nature-rnInspired algorithm; Swarm Intelligence based algorithm; Optimization.
A Novel Control Approach of DC Motor Drive with Optimization Techniques
by Nagendra Swarnkar, Rizwana Khokhar, Mahendra Lalwani
Abstract: Any machine either AC or DC is a backbone of the industry according to their scientific applications or to complete a particular task in industry. Sometimes, industries face the problem due to speed, position, flexibility, reliability and higher cost. To overcome these problems, numbers of controllers are available in industries such as PI controller (Proportional Integral), PD controllers (Proportional Derivatives) and PID controller (Proportional Integral Derivatives). This paper presents PID controller which is simply tuned with the nature-inspired algorithm that gives better dynamic and static performance with high accuracy. The main aim of this paper is to obtain better performance of a DC machine by using PID controller which is tuned with particle swarm optimization (PSO) and ant colony optimization techniques (ACO) algorithms with different error functions such as integral time-weighted absolute error (ITAE), integral square error (ISE) and integral absolute error (IAE).
Keywords: DC motor; proportional integral derivative (PID) controller; performance indices.
Special Issue on: Design, Analysis and Applications of Recent Swarm Intelligence-based Optimisation Algorithms
Landmark operator inspired artificial bee colony algorithm for optimal vector control of induction motor
by Fani Bhushan Sharma, Shashi Raj Kapoor
Abstract: In recent years, soft computing strategies have played a vital role to solve optimization problems associated with the real world. In this paper, an efficient soft computing strategy namely, artificial bee colony algorithm (ABC_algo) is modified with incorporating landmark operator. The proposed modified ABC algorithm is named as land mark inspired ABC (LMABC). The performance of LMABC is evaluated on benchmark functions. Further, the proposed LMABC is applied for vector control of induction motor (IM) and subsequently to improve its efficiency. The vector control of IM includes control of magnitude and phase of each phase current and voltage. In this research paper, the field orientated control, a digital implementation which demonstrates the capability of performing direct torque control, of handling system limitations and of achieving higher power conversion efficiency is considered. The obtained outcomes are significantly better than other state-of-art algorithms available in the literature.
Keywords: Swarm intelligence; Landmark; Induction motor; Metaheuristics; Real world optimization.