Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

Optimization of Concrete-Filled Steel Tubular (CFST) Columns Using Meta-Heuristic Algorithms

Concrete-filled steel tubular (CFST) columns are an extensively studied area due to the favorable structural characteristics of these members. In order obtain the best possible performance from these structures while reducing the cost the use of optimization algorithms is indispensable. For this reason, meta-heuristic algorithms are finding increasing application in engineering due to their high efficiency. Various equations that predict the axial ultimate load-carrying capacity (Nu) of CFST columns are available in design codes as well as the research literature. However, most of these equations are only applicable within certain parameter ranges. A recently developed set of equations that have better parameter ranges of applicability was applied in this study. Furthermore, a newly developed meta-heuristic algorithm called social spider algorithm is applied to the cross-section optimization of circular CFST columns. The improvement of the structural dimensioning under Nu constraint was demonstrated.

A New Bio-Inspired Social Spider Algorithm

The concept of bio-inspired algorithms is used in real-world problems to search the efficient problem-solving methods. Evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques of metahuristics. In this paper, an effort has been made to propose a modified social spider algorithm to solve global optimization problems in the real world. Social spiders used the foraging strategy, vibrations on the spider web to determine the positions of prey. The selection of vibration, estimated new position and calculation of the fitness function, has been furnished in details way as compared to different previously proposed swarm intelligence algorithms. Moreover, experimental result has been carried out by modified social spider on series of widely-used benchmark problem with four benchmark algorithms. Furthermore, a modified form of the proposed algorithm has superior performance as compared to other state-of-the-art metaheuristics algorithms.

Optimal reactive power dispatch using θ-social mimic optimization (θ-SMO)

The social mimic optimization algorithm (SMO) and its enhanced version (θ-SMO) is presented in the current study for the optimal dispatch problem of the reactive power (ORPD) with continuous and discrete control variables in the IEEE standard networks. The feasibleness and functioning of the θ-SMO and SMO algorithms are indicated for the IEEE 57-bus, and IEEE 118-bus standard networks. The outcomes of the simulation were compared, and it was shown that the optimization efficacy of these algorithms is higher than other rooted algorithms, such as optics in-spired optimization (OIO), the social spider algorithm (SSA) algorithm, and biogeography-based optimization (BBO). Results obtained for ORPD problem indicate better performance concerning the θ-SMO algorithm’s solution quality compared to original SMO algorithm and other algorithms.