Surafel Luleseged Tilahun

Modified Firefly Algorithm

Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.

Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems

Nature-inspired optimization algorithms have become useful in solving difficult optimization problems in different disciplines. Since the introduction of evolutionary algorithms several studies have been conducted on the development of metaheuristic optimization algorithms. Most of these algorithms are inspired by biological phenomenon. In this paper, we introduce a new algorithm inspired by prey-predator interaction of animals. In the algorithm randomly generated solutions are assigned as a predator and preys depending on their performance on the objective function. Their performance can be expressed numerically and is called the survival value. A prey will run towards the pack of preys with better surviving values and away from the predator. The predator chases the prey with the smallest survival value. However, the best prey or the prey with the best survival value performs a local search. Hence the best prey focuses fully on exploitation while the other solution members focus on the exploration of the solution space. The algorithm is tested on selected well-known test problems and a comparison is also done between our algorithm, genetic algorithm and particle swarm optimization. From the simulation result, it is shown that on the selected test problems prey-predator algorithm performs better in achieving the optimal value.

Extended Prey-Predator Algorithm with a Group Hunting Scenario

Prey-predator algorithm (PPA) is a metaheuristic algorithm inspired by the interaction between a predator and its prey. In the algorithm, the worst performing solution, called the predator, works as an agent for exploration whereas the better performing solution, called the best prey, works as an agent for exploitation. In this paper, PPA is extended to a new version called nm-PPA by modifying the number of predators and also best preys. In nm-PPA, there will be n best preys and m predators. Increasing the value of n increases the exploitation and increasing the value of m increases the exploration property of the algorithm. Hence, it is possible to adjust the degree of exploration and exploitation as needed by adjusting the values of n and m. A guideline on setting parameter values will also be discussed along with a new way of measuring performance of an algorithm for multimodal problems. A simulation is also done to test the algorithm using well known eight benchmark problems of different properties and different dimensions ranging from two to twelve showing that nm-PPA is found to be effective in achieving multiple solutions in multimodal problems and also has better ability to overcome being trapped in local optimal solutions.

A new algorithm for multilevel optimization problems using evolutionary strategy, inspired by natural adaptation

Multilevel optimization problems deals with mathematical programming problems whose feasible set is implicitly determined by a sequence of nested optimization problems. These kind of problems are common in different applications where there is a hierarchy of decision makers exists. Solving such problems has been a challenge especially when they are non linear and non convex. In this paper we introduce a new algorithm, inspired by natural adaptation, using (1+1)-evolutionary strategy iteratively. Suppose there are k level optimization problem. First, the leaders level will be solved alone for all the variables under all the constraint set. Then that solution will adapt itself according to the objective function in each level going through all the levels down. When a particular levels optimization problem is solved the solution will be adapted the levels variable while the other variables remain being a fixed parameter. This updating process of the solution continues until a stopping criterion is met. Bilevel and trilevel optimization problems are used to show how the algorithm works. From the simulation result on the two problems, it is shown that it is promising to uses the proposed metaheuristic algorithm in solving multilevel optimization problems.

Prey predator algorithm with adaptive step length

Prey predator algorithm is a swarm-based metaheuristic algorithm inspired by the interaction between a predator and its prey. The worst performing solution from the solution set is called a predator, the best preforming solution is called best prey and the rest are called ordinary prey. The predator focuses on exploration while the best prey totally focuses on exploitation. Parameter assignments, especially step length, plays an important role in rapid convergence of the solution to the optimal solution. If the step length is too short, the algorithm will take more time to converge whereas if it is too big, then the algorithm will oscillates by jumping over the solution, making it hard to obtain the desired quality of solution. In this paper, adaptive step length for prey predator algorithm will be used to produce a rapid convergence. The study is also supported by simulation results with appropriate statistical analysis.

Comparative Study of Prey Predator Algorithm and Firefly Algorithm

AbstractMetaheuristic algorithms are found to be promising for difficult and high dimensional problems. Most of these algorithms are inspired by different natural phenomena. Currently, there are hundreds of these metaheuristic algorithms introduced and used. The introduction of new algorithm has been one of the issues researchers focused in the past fifteen years. However, there is a critic that some of the new algorithms are not in fact new in terms of their search behavior. Hence, a comparative study in between existing algorithms to highlight their differences and similarity needs to be studied. Apart from knowing the similarity and difference in search mechanisms of these algorithms it will also help to set criteria on when to use these algorithms. In this paper a comparative study of prey predator algorithm and firefly algorithm will be discussed. The discussion will also be supported by simulation results on selected twenty benchmark problems with different properties. A statistical analysis called ...

Grey theory to predict Ethiopian foreign currency exchange rate

A system containing known values and uncertain unknown values is called a Grey system. Grey system requires only a limited amount of data to estimate the behaviour of unknown systems with poor, incomplete or uncertain information. In this paper, the accuracies of different Grey system models such as GM(1,1), FRMGM(1,1), VGM and FRMVGM are investigated. In addition to this, Linear Regression model is also used for comparison. These Grey models solve complex and sophisticated problems like foreign currency exchange. Foreign currency exchange rates are affected by many highly correlated factors. These factors could be economic, political and even psychological factors, and each of them affect the rate of currency exchange in difference level from time to time. Foreign currency exchange rate from Commercial Bank of Ethiopia between November 2014 and October 2015 are used to compare the performance of different models. The simulation result shows that FRMGM(1,1) is the best in model fitting and forecasting foreign currency exchange.