Mosquito Flying Optimization ( Mfo )

Differential Evolution, or DE, is a cost minimisation method that utilises various evolutionary algorithm concepts, but can also handle non-differentiable, nonlinear, and multimodal objective functions that standard evolutionary algorithms cannot. Experiments have shown that DE shows good convergence properties and outperforms other EA’s, converging faster and with more certainty than many other popular global optimization methods.

[6] Aleksandar Shurbevski, Noriaki Hirosue, Hiroshi Nagamochi,"Optimization Techniques for Robot Path Planning",Advances in Intelligent Systems and Computing 231,2013.

Dynamic Programming is an algorithm paradigm for solving optimization problems that broke the problem into sub problems. Each of the sub-problems solves only once and results are stored in a table rather than solving again and again. Then the table is used to retrieve the solution of original problem. The dynamic programming is based on bottom-up approach.

The vast majority of the optimization problems emerging in real-world applications incorporate vast numbers of variables and constraints, which

no restriction on the shape of the cost curves and also heuristic methods do not always guarantee discovering the global optimal solution in finite time, the often provide a fast and reasonable solution.

The most challenging issue in validation of an Evolutionary Multi-objective Optimization (EMOO) algorithm is to identify the right benchmark functions with diverse characteristics such as multi-modality, deception, isolation and particularly location of true Pareto-optimal front in the surface to resemble complicated real life problems. Traditional benchmark functions [1], [2] usually have the global optimum lying either in the centre of the search range or on the bounds. Naturally, these benchmark functions are inadequate to exhaustively test the performance of a MOO algorithm. In order to overcome the above problem, a set of recommended benchmark functions [4] was proposed in the

Huang et al. [23] introduced a sensor optimization approach based on genetic algorithm for the multiple

where are all constants. The objective of this modification was to raise the global search over the entire search space during the early part of the optimization and to encourage the particles to converge to global optima at the end of the search. The authors referred this as the “PSO-TVAC” (PSO with Time-Varying-Acceleration-Coefficients) method. Indeed, C1 was decreased from 2.5 to 0.5 whereas C2 was increased from 0.5 to 2.5. {209}

— The paper reviews the state-of-the-art nature inspired metaheuristic algorithms in optimization, including the Firefly algorithm, PSO algorithms and ABC algorithm. By implementing them in Matlab, we will use worked examples to show how each algorithm works. Firefly algorithm is an evolutionary optimization algorithm, and is inspired by the flashing behavior of special flies called fireflies in nature. There are some noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. Firefly algorithm is one of the new metaheuristic algorithms for these optimization problems. The algorithm is inspired by the flashing behavior of fireflies. Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, which simulates the flash pattern and characteristics of fireflies. It is a powerful swarm intelligence algorithm inspired by the flashing phenomenon of the fireflies. The optimization results of both PSO and Firefly are analyzed from the results obtained in Matlab and the results are used to compare both the algorithms.

[38] observed that PSO, its modification and hybridization with other algorithms gives better results in

In this study, the discussion on a number of important issues and the state-of-the-art development of the Model for Optimizing Antenna Array Element is done. An optimal radiation pattern as well as Minimum Side lobes levels are obtained for a Rectangular antenna array using the hybridization of particle swarm and genetic algorithm optimization technique. A set of normalized complex and phase shift weights are generated by the developed optimization algorithm and the bound constrained fitness function that allows the optimization for non-uniform element spacing is presented. A comparison between the un-optimized pattern and the one optimized for minimization of SLL using the model developed in this research is also presented, the results

The two main feature of optimization algorithms is exploitation and exploration. Exploration is an ability of the algorithm to explore a different region of search space in a manner to locate good optimum while exploitation is the ability to concentrate the search around the promising area in order to refine a candidate solution. Also, exploration deals with global optimal and exploitation provide local optimization and to handle these feature inertia weight parameter (w) is used in modified PSO. A Higher value of ‘w’ gives better exploration and lower value of ‘w’ gives better exploitation [4,9].

This approach propose a “fuzzy-multi-objective particle swarm optimization” (FMOPSO) for solving TCQT problem. The parameters of cost, time and quality are defined by fuzzy numbers and a “fuzzy multi attribute utility” procedure is used with limits of fuzzy arithmetic operation to adopt and evaluate the selected construction methods. The proposed method is justified and implemented through computational analyses.

This set is passed to OPF again to check for feasibility and if it is impossible to improve the solution with those rules then the process stops. The best candidate is selected as a solution after the process is repeated for a specified number of times. The paper which we are referring [1] indicates that the solution obtained by heuristic method is better than that of LR and GA and the time taken to produce the solution is significantly less than those two method.

Moth-Flame optimization (MFO) Algorithm, is applied for constrained optimization and engineering design problems. A comparative analysis of MFO algorithm expresses the optimum functional value in term of accuracy and standard deviation over rest of well-known constraint