Chaos is embedded to the he charged system search (CSS) to solve practical optimization problems. To improve the ability of global search, different chaotic maps are introduced and three chaotic-CSS methods are developed. A comparison of these variants and the standard CSS demonstrates the superiority and suitability of the selected variants for practical civil optimization problems.

Tunnel structures are known as expensive infrastructures and determining optimum designs of these structures can play a great role in minimizing their cost. The formulation of optimum design of industrial tunnel sections as an optimization is considered in this paper and then the enhanced charged system search, as a recently developed meta-heuristic approach, has been applied to solve the problem. The results and comparisons based on numerical examples show the efficiency of the optimization algorithm.

The charged system search (CSS) is combined to chaos to solve mathematical global optimization problems. The CSS is a recently developed meta-heuristic optimization technique inspired by the governing laws of physics and mechanics. The present study introduces chaos into the CSS in order to increase its global search mobility for a better global optimization. Nine chaos-based CSS (CCSS) methods are developed, and then for each variant, the performance of ten different chaotic maps is investigated to identify the most powerful variant. A comparison of these variants and the standard CSS demonstrates the superiority and suitability of the selected variants for the benchmark mathematical optimization problems.

1. Introduction: With increasing demand for large scale and complex structural models, optimum design has become a challenging and important issue in structural engineering and meta-heuristic algorithms have become powerful tools for optimal design of structures. charged system search (CSS) is one of the meta-heuristics, inspired by the governing laws of electrical physics and the Newtonian mechanics, Kaveh and Talatahari (2010). CSS based optimum design of structures needs adjustments for variable based and problem based constraints. …

A charged system search algorithm (CSS) is applied to the optimal cost design of water distribution networks. This algorithm is inspired by the Coulomb and Gauss’s laws of electrostatics in physics. The CSS utilizes a number of charged particles which influence each other based on their fitness values and their separation distances considering the governing law of Coulomb. The well-known benchmark instances, Hanoi network, double Hanoi network, and New York City tunnel problem, are utilized as the case studies to evaluate the optimization performance of CSS. Comparison of the results of the CSS with some other meta-heuristic algorithms indicates the performance of the new algorithm.

Composite open channel structures are an expensive infrastructure in terms of material, construction and maintenance, and therefore finding the optimum design becomes an important issue. This paper develops four different models for composite open channel and applies a new optimization algorithm to determine the optimal design of these models. The new algorithm called charged system search (CSS) is inspired by the governing laws from electrostatics physics and mechanics. CSS is a multi-agent approach and each agent is a charged Particle (CP) which can affect others based on their fitness values and their separation distances. The results of the CSS are compared to those of ant colony optimization and the genetic algorithm method to highlight its superiority for determining the optimum design of composite open channel structures.

In this paper, the recently developed meta-heuristic optimization algorithm, known as the charged system search (CSS), is utilized for the problems of facility locations on a network.The CSS is an optimization algorithm that acts on the basis of the interaction between charged particles (CPs) considering the governing laws of Coulomb and Gauss from electrical physics and laws of motion from Newtonian mechanics.