Hybrid Metaheuristics

Research and Applications

Siddhartha Bhattacharyya

• Publisher: WSPC
• Year: 2018
• ISBN:
  9789813270244
• Topic: Computer Science
• Subtopic: Programming Algorithms
• Index: Computer Science

A metaheuristic is a higher-level procedure designed to select a partial search algorithm that may lead to a good solution to an optimization problem, especially with incomplete or imperfect information.

This unique compendium focuses on the insights of hybrid metaheuristics. It illustrates the recent researches on evolving novel hybrid metaheuristic algorithms, and prominently highlights its diverse application areas. As such, the book helps readers to grasp the essentials of hybrid metaheuristics and to address real world problems.

The must-have volume serves as an inspiring read for professionals, researchers, academics and graduate students in the fields of artificial intelligence, robotics and machine learning.

Contents:

• Preface
• Introduction to Hybrid Metaheuristics (Sandip Dey, Sourav De and Siddhartha Bhattacharyya)
• Research:
  • Hybrid TLBO-GSA Strategy for Constrained and Unconstrained Engineering Optimization Functions (Alok Kumar Shukla, Pradeep Singh and Manu Vardhan)
  • Review on Hybrid Metaheuristic Approaches for Optimization in Multibiometric Authentication System (Aarohi Vora, Chirag Paunwala and Mita Paunwala)
  • A Novel Membrane Computing Inspired Jaya Algorithm Based Automatic Generation Control of Multi-area Interconnected Power System (Tapan Prakash and Vinay Pratap Singh)
• Applications:
  • Edge Detection in Underwater Image Based on Human Psycho Visual Phenomenon and Mean Particle Swam Optimization (MeanPSO) (Hiranmoy Roy and Soumyadip Dhar)
  • Quantum Inspired Non-dominated Sorting Based Multi-objective GA for Multi-level Image Thresholding (Sandip Dey, Siddhartha Bhattacharyya and Ujjwal Maulik)
  • An Optimized Support Vector Regression Using Whale Optimization for Long Term Wind Speed Forecasting (Sarah Osama, Essam H Houssein, Ashraf Darwish, Aboul Ella Hassanien and Aly A Fahmy)
  • A Hybrid Grey Wolf Optimization and Support Vector Machines for Detection of Epileptic Seizure (Asmaa Hamad, Essam H Houssein, Aboul Ella Hassanien, Aly A Fahmy and Siddhartha Bhattacharyya)
  • Optimization of Recurrent Neural Networks Using Evolutionary Group-based Particle Swarm Optimization for Hexapod Robot Gait Generation (Chia-Feng Juang, Yu-Cheng Chang and I-Fang Chung)
  • Load Optimization using Hybrid Metaheuristics in Power Generation with Transmission Loss (Dipankar Santra, Krishna Sarker, Anirban Mukherjee and Subrata Mondal)
  • Conclusion (Siddhartha Bhattacharyya)

Readership: Professionals, researchers, academics, and graduate students in artificial intelligence, robotics and machine learning.

Chapter 1

Introduction to Hybrid Metaheuristics

Sandip Dey

Department of Computer Science & Engineering, OmDayal Group of Institutions, Birshibpur, Howrah-711316, India
[email protected]

Sourav De

Department of Computer Science & Engineering, Cooch Behar Government Engineering College, Cooch Behar, India
[email protected]

Siddhartha Bhattacharyya

Department of Computer Application, RCC Institute of Information Technology, Canal South Road, Beliaghata, Kolkata-700015, India
[email protected]

The action of combining the components from various algorithms is presently the utmost successful and effective trend in optimization. The foremost motivation behind the hybridization of diverse algorithmic ideas is to acquire better performing systems, which exploit and coalesce benefits of the different pure approaches, that is, hybrid systems that are supposed to be benefited from synergy. Actually, taking a suitable amalgamation of multiple algorithmic notions is often the key to achieve highest performance in solving number of hard (complex) optimization problems. Nonetheless, evolving an exceedingly effective hybrid method is not a simple task at all. The hybridization of popular metaheuristics like particle swarm optimization, evolutionary algorithms, simulated annealing, variable neighborhood search, and ant colony optimization with techniques from other fields like artificial intelligence, operations research forms the basis of evolving more efficient and robust solutions.

Keywords: Hybrid metaheuristics, quantum computing, metaheuristics, optimization, and advanced hybrid metaheuristics.

1. Introduction

Optimization is required in different fields of engineering such as mechanical engineering, civil engineering, mining engineering, nanoscience and nanoengineering, computer, communication, networking and information engineering, bioinformatics and biomedical engineering, etc. to obtain better solutions. To solve optimization problems practically in those above mentioned fields, some efficient and effective computational algorithms are very mush essential. The foremost objective of the optimization is to derive the optimal solution for a given problem. An optimization problem is determined as: deriving values of the variables that maximize or minimize the fitness (objective) function(s) in line with the constraints.¹ These kinds of problems are based on three factors: firstly, they solve some minimization/- maximization objective function(s), secondly, a set of unknown variables those are involve in the objective functions and thirdly, a set of constraints that permit the unknowns for taking some specific values but exclude others.¹

Most of the optimization problems may have more than one local solutions. In this circumstances, choosing of the optimization method is very much important. The optimization method must not be greedy and the searching process will not be localized in the neighborhood of the best solution as it may stick at a local solution and that will misguide the search process. It should be observed that the optimization algorithm should make a balance between global and local search. Both the mathematical and combinatorial types optimization problems can be solved by different methods. The optimization problems that have large search space or more complex in nature will become difficult to solve using conventional mathematical optimization algorithms. Here is the utility of the metaheuristic algorithms. Different metaheuristic optimization algorithms, present in the research arena are very much capable to solve difficult optimization problems.

A heuristic method can be noted as the way of solving, leaning or discovery of a problem using practical methods and ultimately, will derive immediate near optimal results rather than exact results. Basically, a metaheuristic is an iterative generation procedure to solve a subordinate heuristic by syndicating intelligently different concepts to explore and exploit the search space, learning strategies are applied to structure information in order to find efficiently near-optimal solutions.² The main objective of metaheuristic is to derive a set of optimal solutions which is large enough to be completely sampled. The popularity of using metaheuristic techniques on a variety of problems is due to the fact that any conventional algorithm cannot manage many real world problems, in spite of the raising computational power, simply due to unrealistically large running times.³ These algorithms make few assumptions to solve the optimization problems. It is not guaranteed that the metaheuristics may generate globally optimal solutions to solve some class of problems since most of the implementations are some form of stochastic optimization and the resultant solutions may dependents on the set of generated random variables. Metaheuristic algorithms are advantageous over optimization algorithms, simple heuristics, or iterative methods as they often determine good solutions with a lesser computational effort by exploring a large set of feasible solutions.

Most well-known metaheuristic algorithms are Genetic algorithm (GA),⁴ simulated annealing (SA),⁵ Tabu search (TS)^6–8 and different types of swarm intelligence algorithms. Genetic algorithm (GA) works on the principle of the evolutionary process in nature. Tabu search applies the memory structure in living beings, whereas simulated annealing imitates the annealing process in crystalline solids. Some well known and recognized swarm intelligence algorithms are particle swarm optimization (PSO),⁹ ant colony optimization (ACO),¹⁰ artificial bee colony optimization (ABC),¹¹ differential opti...