Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
The organization of work in social insect colonies
Complexity - Special issue: Selection, tinkering, and emergence in complex networks
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Particle swarm optimization with adaptive population size and its application
Applied Soft Computing
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Engineering Optimization: An Introduction with Metaheuristic Applications
Engineering Optimization: An Introduction with Metaheuristic Applications
The best-so-far selection in Artificial Bee Colony algorithm
Applied Soft Computing
A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems
Applied Soft Computing
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
Applied Soft Computing
Mixed variable structural optimization using Firefly Algorithm
Computers and Structures
Diversity enhanced particle swarm optimization with neighborhood search
Information Sciences: an International Journal
An efficient and robust artificial bee colony algorithm for numerical optimization
Computers and Operations Research
Hi-index | 12.05 |
During the past decade, solving constrained optimization problems with swarm algorithms has received considerable attention among researchers and practitioners. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO-C) is proposed for solving constrained optimization tasks. The SSO-C 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. For constraint handling, the proposed algorithm incorporates the combination of two different paradigms in order to direct the search towards feasible regions of the search space. In particular, it has been added: (1) a penalty function which introduces a tendency term into the original objective function to penalize constraint violations in order to solve a constrained problem as an unconstrained one; (2) a feasibility criterion to bias the generation of new individuals toward feasible regions increasing also their probability of getting better solutions. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. Simulation and comparisons based on several well-studied benchmarks functions and real-world engineering problems demonstrate the effectiveness, efficiency and stability of the proposed method.