In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html.

Moth-flame optimization algorithm

The Ant Lion Optimizer

https://cyberleninka.org/article/n/1407659.pdf

Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

Nonlinear System Identification

A survey on new generation metaheuristic algorithms

In this paper a novel evolutionary algorithm, suitable for continuous nonlinear optimization problems, is introduced. This optimization algorithm is inspired by the life of a bird family, called Cuckoo. Special lifestyle of these birds and their characteristics in egg laying and breeding has been the basic motivation for development of this new evolutionary optimization algorithm. Similar to other evolutionary methods, Cuckoo Optimization Algorithm (COA) starts with an initial population. The cuckoo population, in different societies, is in two types: mature cuckoos and eggs. The effort to survive among cuckoos constitutes the basis of Cuckoo Optimization Algorithm. During the survival competition some of the cuckoos or their eggs, demise. The survived cuckoo societies immigrate to a better environment and start reproducing and laying eggs. Cuckoos' survival effort hopefully converges to a state that there is only one cuckoo society, all with the same profit values. Application of the proposed algorithm to some benchmark functions and a real problem has proven its capability to deal with difficult optimization problems.

Cuckoo Optimization Algorithm

Purpose – This paper aims to describe colonial competitive algorithm (CCA), a novel socio‐politically inspired optimization strategy, and how it is used to solve real world engineering problems by applying it to the problem of designing a multivariable proportional‐integral‐derivative (PID) controller Unlike other evolutionary optimization algorithms, CCA is inspired from a socio‐political process – the competition among imperialists and colonies In this paper, CCA is used to tune the parameters of a multivariable PID controller for a typical distillation column processDesign/methodology/approach – The controller design objective was to tune the PID controller parameters so that the integral of absolute errors, overshoots and undershoots be minimized This multi‐objective optimization problem is converted to a mono‐objective one by adding up all the objective functions in which the absolute integral of errors is emphasized to be reduced as long as the overshoots and undershoots remain acceptableFindin

https://www.icasite.info/icasite/ica_papers/new_code/icapap_2008_en_esmae_colonialco.pdf

Colonial competitive algorithm A novel approach for PID controller design in MIMO distillation column process

This paper presents an application of Colonial Competitive Algorithm (CCA) in game theory and multi-objective optimization problems. The recently introduced CCA has proven its excellent capabilities, such as faster convergence and better global optimum achievement. In this paper CCA is used to find Nash Equilibrium points of nonlinear non-cooperative games. The proposed method can also be used as an alternative approach to solve multi-objective optimization problems. The effectiveness of the proposed method, in comparison to Genetic Algorithm, is proven through several static and dynamic example games and also multi-objective problems.

Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2008/data/papers/1096.pdf

Decentralized PID Controller Design for a MIMO Evaporator Based on Colonial Competitive Algorithm

Applications of the boost DC-DC converters are becoming popular due to their conformity to renewable energy systems. PI controllers are the most used controllers in power electronic circuit and systems controlling. This paper presents the optimal tuning of Proportional-Integral (P-I) controller for DC- DC boost converters based on Cuckoo optimization Algorithm (COA). The COA based optimization of boost converter is modeled and simulated in MATLAB/Simulink environment under variable load. Then the simulation results of the COA based tuned PI controller are compared to the classical PI tuning method. The performance of proposed COA based tuning PI was better than conventional PI tuning method.

Ramin Rajabioun

Papers

Cuckoo Optimization Algorithm

Colonial competitive algorithm A novel approach for PID controller design in MIMO distillation column process

Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement

Decentralized PID Controller Design for a MIMO Evaporator Based on Colonial Competitive Algorithm

Optimal Tuning of PI Controller for Boost DC-DC Converters Based on Cuckoo Optimization Algorithm