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

Abstract

Nature-inspired optimization algorithms have become useful in solving di±cult optimization problems in di®erent 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.

Particle size estimate of grinding processes using random vector functional link networks with improved robustness

“…As a consequence, only α 1 , α 2 , α 3 , α 5 , β 1 , β 2 ,β 3 , θ 1 , θ 2 ,θ 3 ,θ 5 , θ 7 ,θ 8 need to be optimized. The preypredator optimization (PPA) algorithm [21], a most recent metaheuristic optimization technique, is adopted to find their optimal values.…”

“…The optimization producer is briefly viewed here, but for more detailed information please refer to the literature [21]. In PPA, there are three types of animals.…”

“…Therefore, this paper incorporates the WLS into the original RVFLN to improve the robustness of the resulting learning model with respect to outliers. For the robust RVFLN, some penalty weights p i are added into the SSE cost function (21) to have…”

A comparison between firefly and prey-predator algorithms based on artificial neural networks

“…They do not use any gradient information, and have more chance to avoid local optima because of exploration search space [5]. Prey-predator and Firefly algorithms are straightforward metaheuristic algorithms with higher effectiveness in applying and use [6]. These algorithms are used for continuous and non-continuous optimization problems [7].…”

“…After that, the conclusion is given 2 Optimization algorithms Prey-predator algorithm-PPA is a swarm based metaheuristic algorithm developed for handling complex optimization applications in engineering, transportation, management, economics, artificial intelligence, and decision sciences etc. [2,6,17,18]. The algorithm mimics the ways in which a predator runs after and hunt its prey where each prey tries to stay within the pack, search for a hiding place, and run away from the predator.…”