A swarm optimization algorithm inspired in the behavior of the social-spider (1406.3282v1)

Abstract: 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 paper introduces a novel Social Spider Optimization (SSO) algorithm that mimics gender-specific behaviors of social spiders to enhance exploration and exploitation in optimization tasks.
• It leverages unique mechanisms like vibration modeling and targeted mating strategies to outperform traditional swarm techniques such as PSO and ABC across benchmark functions.
• The study demonstrates SSO's superior convergence speed and solution quality, paving the way for future research in dynamic and hybrid bio-inspired optimization methods.

Social Spider Optimization: A Bio-Inspired Approach for Complex Optimization Problems

Overview

The paper introduced a novel optimization algorithm, Social Spider Optimization (SSO), which draws inspiration from the cooperative behavior of social spiders. SSO models the interaction between two types of search agents, male and female spiders, to solve complex optimization problems. This bio-inspired framework seeks to exploit the natural division of tasks and roles observed in social spider colonies to enhance the algorithm's exploration and exploitation capabilities.

Key Concepts

SSO leverages the gender differentiation within social spider colonies, where females are typically more numerous and social, while males focus on reproduction. This division is crucial in driving the dynamics of the algorithm:

• Female Spiders: Females exhibit cooperative behaviors that either attract or repel them from other members based on perceived vibrations through a communal web. This process encourages diverse exploration and mitigates premature convergence.
• Male Spiders: Males are categorized into dominant and non-dominant groups. Dominant males pursue nearby females for mating, promoting substantial diversity, while non-dominant males flock towards the population's center for resource optimization.

Algorithmic Details

SSO introduces specific evolutionary operators tailored to each gender role. These operators include interaction mechanisms modeled through vibrations and mating operations designed to enhance information sharing and search robustness. The fusion of these operators with the associated communication mechanisms draws a clear parallel to natural systems observed in social spiders.

• Vibration Modeling: Vibronic signals, determined by weight (fitness) and distance, influence attraction/repulsion and mating behaviors.
• Mating Strategy: Only dominant males within a certain range engage in mating, contributing to population diversity and adaptive learning.
• Gender-based Operators: Female and male-specific strategies facilitate targeted exploration and exploitation, addressing pitfalls like local minima entrapment associated with algorithms like PSO and ABC.

Empirical Evaluation

The paper presents a thorough experimental comparison of SSO against established swarm optimization techniques, PSO and ABC, across 19 benchmark functions. Results demonstrate SSO's superior performance in terms of convergence speed and solution quality, showcasing its robustness and reliability.

• Numerical Results: SSO consistently delivered optimal or near-optimal solutions faster than PSO and ABC. Its adaptive exploration-exploitation balance proved crucial in overcoming benchmark challenges efficiently.

Implications and Future Directions

The SSO algorithm presents a significant advancement in swarm optimization methodologies, particularly in its unique application of gender differentiation and task specialization. These concepts pave the way for further exploration of bio-inspired algorithms, enhancing optimization processes in diverse fields such as robotics, network design, and beyond.

Future research could explore hybridization with other bio-inspired heuristics or adapt the SSO framework for dynamic, real-time optimization scenarios. Additionally, refining the modeling of spider communication and task execution strategies may lead to further performance enhancements.

In conclusion, the SSO algorithm effectively harnesses the intricacies of social spider behavior, offering a refined mechanism for tackling complex global optimization problems while addressing the limitations of traditional swarm algorithms.