Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Nature-Inspired Optimization Algorithms

Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications

Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very effi cient and thus have become popular tools for solving real-world problems. Some algorithms are insuffici ently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.

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A Brief Review of Nature-Inspired Algorithms for Optimization

A survey on new generation metaheuristic algorithms

In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems ASO mathematically models and mimics the atomic motion model in nature, where atoms interact through interaction forces resulting from the Lennard-Jones potential and constraint forces resulting from the bond-length potential The proposed algorithm is simple and easy to implement ASO is tested on a range of benchmark functions to verify its validity, qualitatively and quantitatively, and then applied to a hydrogeologic parameter estimation problem with success The results demonstrate that ASO is superior to some classic and newly emerging algorithms in the literature and is a promising solution to real-world engineering problems

Atom search optimization and its application to solve a hydrogeologic parameter estimation problem

Logarithmic spiral phenomena are often appeared in nature. In this letter, we show that a logarithmic spiral can realize an effective strategy regarding the metaheuristics and propose a new two-dimensional metaheuristics called spiral optimization utilizing a feature of the logarithmic spiral. The simulation results for two-dimensional benchmark problems show the effectiveness and the potential of the proposed method. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Primary study of spiral dynamics inspired optimization

Spiral Dynamics Inspired Optimization

Metaheuristics is a framework of practical methods for global optimization problems. We recently proposed a new metaheuristics method inspired from spiral phenomena in nature which is called spiral optimization. However, the spiral optimization was restricted to 2-dimensional continuous optimization problems. In this paper, we develop a spiral optimization method for n-dimensional continuous optimization problems by constructing an n-dimensional spiral model. The n-dimensional spiral model is designed using rotation matrices in n-dimensional space. Simulation results for different benchmark problems show the effectiveness of our proposal compared to PSO and DE.

Spiral Multipoint Search for Global Optimization

Recently we proposed a new multipoint search method in metahuristics for only 2-dimensional continuous optimization problems based on analogy of spiral phenomena in nature which is called 2-dimensional spiral optimization. The focused spiral phenomena which appear frequently in nature are approximated to logarithmic spirals. The 2-dimensional spiral optimization utilizes a feature of logarithmic spirals. In this paper, we propose n-dimensional spiral optimization by extending 2-dimensional one. The n-dimensional spiral model is constructed based on rotation matrices defined in n-dimensional space. Simulation results for various benchmark problems show effectiveness of the proposed method in comparison with other metaheuristics.

Spiral optimization -A new multipoint search method

This paper presents a hierarchical optimization method for determining optimal operation of a distributed energy system which consists of a distribution network and customers with dispersed generation and storage systems. The optimal operation problem of a distributed energy system is divided into two subproblems; optimal operation of a dispersed energy system for a customer and optimal operation of power flow on distribution network. While Dynamic Programming (DP) is used for solving optimal operation of a dispersed energy system for a customer, successive approximation method is applied for obtaining optimal operation of power flow on distribution network. The practicability and generality of the developed hierarchical optimization method for determining optimal operation of a distributed energy system are evaluated through some simulations using some simple models of a distributed energy system consisting of a distribution network and customers with dispersed generation and storage systems.

Kenichi Tamura

Papers

Primary study of spiral dynamics inspired optimization

Spiral Dynamics Inspired Optimization

Spiral Multipoint Search for Global Optimization

Spiral optimization -A new multipoint search method

Modeling and optimal operation of distributed energy systems via Dynamic Programming