الگوریتم بهینه سازی ازدحام ، الهام گرفته شده از رفتار اجتماعی عنکبوت

Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions.

The collective intelligent behavior of insect or animal groups in nature such as flocks of birds, colonies of ants, schools of fish, swarms of bees and termites have attracted the attention of researchers. The aggregative conduct of insects or animals is known as swarm behavior. Entomologists have studied this collective phenomenon to model biological swarms while engineers have applied these models as a framework for solving complex real-world problems. This branch of artificial intelligence which deals with the collective behavior of swarms through complex interaction of individuals with no supervision is frequently addressed as swarm intelligence. Bonabeau defined swarm intelligence as “any attempt to design algorithms or distributed problem solving devices inspired by the collective behavior of the social insect colonies and other animal societies” (Bonabeau, Dorigo, & Theraulaz, 1999). Swarm intelligence has some advantages such as scalability, fault tolerance, adaptation, speed, modularity, autonomy and parallelism (Kassabalidis, El-Sharkawi, II Marks, Arabshahi, & Gray, 2001). The key components of swarm intelligence are self-organization and labor division. In a self-organizing system, each of the covered units responds to local stimuli individually and may act together to accomplish a global task, via a labor separation which avoids a centralized supervision. The entire system can thus efficiently adapt to internal and external changes. Several swarm algorithms have been developed by a combination of deterministic rules and randomness, mimicking the behavior of insect or animal groups in nature. Such methods include the social behavior of bird flocking and fish schooling such as the Particle Swarm Optimization (PSO) algorithm (Kennedy & Eberhart, 1995), the cooperative behavior of bee colonies such as the Artificial Bee Colony (ABC) technique (Karaboga, 2005), the social foraging behavior of bacteria such as the Bacterial Foraging Optimization Algorithm (BFOA) (Passino, 2002), the simulation of the herding behavior of krill individuals such as the Krill Herd (KH) method (Hossein & Hossein-Alavi, 2012), the mating behavior of firefly insects such as the Firefly (FF) method (Yang, 2010) and the emulation of the lifestyle of cuckoo birds such as the Cuckoo Optimization Algorithm (COA) (Rajabioun, 2011). In particular, insect colonies and animal groups provide a rich set of metaphors for designing swarm optimization algorithms. Such cooperative entities are complex systems that are composed by individuals with different cooperative-tasks where each member tends to reproduce specialized behaviors depending on its gender (Bonabeau, 1998). However, most of swarm algorithms model individuals as unisex entities that perform virtually the same behavior. Under such circumstances, algorithms waste the possibility of adding new and selective operators as a result of considering individuals with different characteristics such as sex, task-responsibility, etc. These operators could incorporate computational mechanisms to improve several important algorithm characteristics including population diversity and searching capacities. Although PSO and ABC are the most popular swarm algorithms for solving complex optimization problems, they present serious flaws such as premature convergence and difficulty to overcome local minima (Wang et al., 2011 and Wan-li and Mei-qing, 2013). The cause for such problems is associated to the operators that modify individual positions. In such algorithms, during their evolution, the position of each agent for the next iteration is updated yielding an attraction towards the position of the best particle seen so-far (in case of PSO) or towards other randomly chosen individuals (in case of ABC). As the algorithm evolves, those behaviors cause that the entire population concentrates around the best particle or diverges without control. It does favors the premature convergence or damage the exploration–exploitation balance (Wang et al., 2013 and Banharnsakun et al., 2011). The interesting and exotic collective behavior of social insects have fascinated and attracted researchers for many years. The collaborative swarming behavior observed in these groups provides survival advantages, where insect aggregations of relatively simple and “unintelligent” individuals can accomplish very complex tasks using only limited local information and simple rules of behavior (Gordon, 2003). Social-spiders are a representative example of social insects (Lubin, 2007). A social-spider is a spider species whose members maintain a set of complex cooperative behaviors (Uetz, 1997). Whereas most spiders are solitary and even aggressive toward other members of their own species, social-spiders show a tendency to live in groups, forming long-lasting aggregations often referred to as colonies (Aviles, 1986). In a social-spider colony, each member, depending on its gender, executes a variety of tasks such as predation, mating, web design, and social interaction (Aviles, 1986 and Burgess, 1982). The web it is an important part of the colony because it is not only used as a common environment for all members, but also as a communication channel among them (Maxence, 2010) Therefore, important information (such as trapped prays or mating possibilities) is transmitted by small vibrations through the web. Such information, considered as a local knowledge, is employed by each member to conduct its own cooperative behavior, influencing simultaneously the social regulation of the colony (Yip & Eric, 2008). In this paper, a novel swarm algorithm, called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of the cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typical in a colony. Different to most of existent swarm algorithms, in the proposed approach, each individual is modeled considering two genders. Such fact allows not only to emulate in a better realistic way the cooperative behavior of the colony, but also to incorporate computational mechanisms to avoid critical flaws commonly present in the popular PSO and ABC algorithms, such as the premature convergence and the incorrect exploration–exploitation balance. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions which are commonly considered in the literature. The results show a high performance of the proposed method for searching a global optimum in several benchmark functions. This paper is organized as follows. In Section 2, we introduce basic biological aspects of the algorithm. In Section 3, the novel SSO algorithm and its characteristics are both described. Section 4 presents the experimental results and the comparative study. Finally, in Section 5, conclusions are drawn.

In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) has been proposed for solving optimization tasks. The SSO algorithm is based on the simulation of the cooperative behavior of social-spiders whose individuals emulate a group of spiders which interact to each other based on the biological laws of a cooperative colony. The algorithm considers two different search agents (spiders): male and female. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors within the colony. In contrast to most of existent swarm algorithms, the proposed approach models each individual considering two genders. Such fact allows not only to emulate the cooperative behavior of the colony in a realistic way, but also to incorporate computational mechanisms to avoid critical flaws commonly delivered by the popular PSO and ABC algorithms, such as the premature convergence and the incorrect exploration–exploitation balance. SSO has been experimentally tested considering a suite of 19 benchmark functions. The performance of SSO has been also compared to the following swarm algorithms: the Particle Swarm Optimization method (PSO) (Uetz, 1997), and the Artificial Bee Colony (ABC) algorithm (Mezura-Montes et al., 2006). Results have confirmed a acceptable performance of the proposed method in terms of the solution quality of the solution for all tested benchmark functions. The SSO’s remarkable performance is associated with two different reasons: (i) their operators allow a better particle distribution in the search space, increasing the algorithm’s ability to find the global optima; and (ii) the division of the population into different individual types, provides the use of different rates between exploration and exploitation during the evolution process.