An optimization algorithm for multimodal functions inspired by collective animal behavior

Abstract

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often demand locating multiple optima within a search space. This article presents a new multimodal optimization algorithm named as the Collective Animal Behavior (CAB). Animal groups, such as schools of fish, flocks of birds, swarms of locusts and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic and to avoid predation. In the proposed algorithm, searcher agents are a group of animals which interact to each other based on the biological laws of collective motion. Experimental results demonstrate that the proposed algorithm is capable of finding global and local optima of benchmark multimodal optimization problems with a higher efficiency in comparison to other methods reported in the literature.Please cite this article as: Cuevas, E., González, M. An optimization algorithm for multimodal functions inspired by collective animal behavior, Soft Computing 17 (3) , (2013), pp. 489-502 This is a preprint copy that has been accepted for publication in Soft Computing 2 algorithm [21], and elitist-population strategies [22]. However, algorithms based on the GA's do not guarantee convergence to global optima because of their poor exploitation capability. GA's exhibit other drawbacks such as the premature convergence which results from the loss of diversity in the population and becomes a common problem when the search continues for several generations. Such drawbacks [23] prevent the GA's from practical interest for several applications.Using a different metaphor, other researchers have employed Artificial Immune Systems (AIS) to solve the multimodal optimization problems. Some examples are the clonal selection algorithm [24] and the artificial immune network (AiNet) [25,26]. Both approaches use some operators and structures which attempt to algorithmically mimic the natural immune system's behavior of human beings and animals.On other hand, many studies have been inspired by animal behavior phenomena in order to develop optimization techniques such as the Particle swarm optimization (PSO) algorithm which models the social behavior of bird flocking or fish schooling [27]. In recent years, there have been several attempts to apply the PSO to multi-modal function optimization problems [28,29]. However, the performance of such approaches presents several flaws when it is compared to the other multi-modal metaheuristic counterparts [26].Recently, the concept of individual-organization [30,31] has been widely used to understand collective behavior of animals. The central principle of individual-organization is that simple repeated interactions between individuals can produce complex behavioral patterns at group level [30,...

A Cumulative Multi-Niching Genetic Algorithm for Multimodal Function Optimization

“…I tuned the parameter values heuristically for best performance on the objective functions. For the MNC, RCS, and CAB algorithms, I began by using the values from [8], [9], and [12], respectively, but found that modification of some parameters gave better results. The meanings of the variables in Table 4 can be found in [12].…”

“…An approach shown to use even fewer function evaluations is an evolutionary algorithm (EA) by Cuevas and Gonźalez that mimics collective animal behaviour [12]. This algorithm models the way animals are attracted to or repelled from dominant individuals, and retains in memory a set of the fittest individuals.…”

“…For further comparison, I also implemented the Collective Animal Behaviour (CAB) evolutionary algorithm [12]. It is a good comparator because it has many common features with multi-niching GAs, but has been shown to give better performance than many of them, particularly in terms of objective function evaluation requirements.…”

Feature Selection Based on Adaptive Particle Swarm Optimization with Leadership Learning

“…Overview of PSO. PSO is a population-based metaheuristic algorithm for simulating the predatory activities of bird and fish populations [45,46], and each particle in the population has two properties: velocity vector v i � (v i1 , v i2 , • • • , v id ) and position vector x i � (x i1 , x i2 , • • • , x id ), where d denotes the dimension. In the search process of PSO, the velocity vectors are dynamically adjusted by the personal best position (pbest i ) and the global best position (gbest) at the current stage, and the position vectors are the candidate solutions to the optimization problems, all of which are updated by equations ( 1)- (2).…”

A novel multi-population coevolution immune optimization algorithm

“…To examine the proposed MCIA, three evolutionary algorithms HABC (Yan et al 2013), the collective animal behavior, CAB (Cuevas and Gonza'lez 2013), and wolf pack algorithm, WPA (Wu et al 2013) are selected for direct comparisons. Here is a brief description of the three algorithms:…”