Recent Researches in Artificial Intelligence and Database Management
A Novel Meta-Heuristic Optimization Algorithm: Current Search Anusorn SAKULIN and Deacha PUANGDOWNREONG* Department of Electrical Engineering, Faculty of Engineering, South-East Asia University 19/1 Petchakasem Rd., Nongkhaem, Bangkok, THAILAND * corresponding author:
[email protected] http://www.sau.ac.th Abstract: - Inspired by an electric current flowing through electric networks, a novel meta-heuristic optimization algorithm named the Current Search (CS) is proposed in this article. The proposed CS algorithm is an optimization algorithm based on the intelligent behavior of electric current flowing through open and short circuits. To perform its effectiveness and robustness, the proposed CS algorithm is tested against five wellknown benchmark continuous multivariable test functions collected by Ali et al. The results obtained by the proposed CS are compared with those obtained by the popular search techniques widely used to solve optimization problems, i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Tabu Search (TS). The results show that the proposed CS outperforms other algorithms. The results obtained by the proposed CS are superior within reasonable time consumed.
Key-Words: - Current Search, Genetic Algorithm, Particle Swarm Optimization, Tabu Search al [16]. Obtained results will be compared with those obtained by GA, PSO, and TS. This article consists of five sections. The CS algorithm is described in section 2. Benchmark continuous multi-dimensional test functions used in this article are given in section 3. Performance evaluation of CS compared with GA, PSO, and TS algorithms against five benchmark multivariable test functions is illustrated in section 4, while conclusion is provided in section 5.
1 Introduction Over five decades, many heuristic algorithms have been developed to solve combinatorial and numeric optimization problems [1]. By literature, several intelligent search techniques, i.e., Evolutionary Programming (EP) [2], Tabu Search [3], Simulated Annealing (SA) [4], Genetic Algorithm (GA) [5], Ant Colony Optimization (ACO) [6], Hit-and-Run (HNR) [7], Hide-and-Seek (HNS) [8], Particle Swarm Optimization (PSO) [9], Harmony Search (HS) [10], Bacterial Foraging Optimization (BFO) [11], Shuffled Frog Leaping Algorithm (SFLA) [12], Bee Colony Optimization (BCO) [13], Key Cutting Search (KCS) [14], and Hunting Search (HuS) [15] etc., have been proposed. These algorithms can be classified into different groups depending on their nature of criteria being considered, such as population-based (EP, GA, ACO, PSO, BFO, BCO, and HuS), neighborhoodbased (TS), iterative-based (SFLA), stochastic (KCS, HNR, and HNS), and deterministic (SA). Among them, GA, PSO, and TS are the most popular intelligent search techniques that are widely used to solve optimization and engineering problems. In this article, the current search (CS), one of the powerful and efficient meta-heuristic optimization search techniques, is proposed. The CS algorithm is inspired by the electric current flowing through electric circuits. The proposed CS algorithm is coded and tested against five benchmark continuous multi-dimensional test functions collected by Ali et
ISBN: 978-1-61804-068-8
2 Current Search Algorithm Based on the principle of current divider in electric circuit theory [17], the electric current flows through all blanch connected in parallel form as can be seen in Fig.1. Each blanch connects to a resistor R having different resistances to obstruct the current. Assume that 0 < R1 < R2 < L < R N . In fundamentals of circuit theory [17], Kirchhoff’s current law (KCL) stats that the algebraic sum of currents entering a node is zero. On the other hand, the sum of the currents entering a node is equal to the sum of the current leaving the node. This means that, in Fig. 1, the sum of all currents in each blanch is equal to the total current supplied by the current source as expressed in (1), where, iT is the total current and i j is the current in blanch j -th. N
∑ i j = iT j =1
125
(1)
Recent Researches in Artificial Intelligence and Database Management
Step 7. If f ( x′) < f ( x0 ) , keep x0 in set Γk and set x0 = x′ , set j = 1 and return to Step 5. Otherwise update j = j + 1 . Step 8. If j < j max , return to Step 5. Otherwise keep x0 in set Ξ and update k = k + 1 . Step 9. Terminate the search process when termination criteria are satisfied. The optimum solution found is x0 . Otherwise return to Step 4. The diagram in Fig. 2 reveals the search process of the proposed CS algorithm.
The behavior of electric current is like a tide that always flow to lower places. The less the resistance of blanch, the more the current flows (see Fig.1, the thickness of arrows representing the current quantity). Referring to Fig. 1, in case of short circuit, the blanch resistance is zero acted as a conductor, while, in case of open circuit, the blanch resistance is infinity acted as an insulator. The Current Search (CS) algorithm is inspired by this concept. All blanches represent the feasible solutions in search space. The local entrapment is occurred when the current hits the open circuit connection. The optimum solution found is the blanch possessing the optimum resistance. i1 iT
i2 i3
iN
R1 R2 R3
RN
i1
blanch
i2
node
Start
Initialize: - search space - k = j = 1, jmax = 10 - N = n = 10, = 0.1 Ø
Uniformly Random set of initial solutions Xi, i=1,…,N within
i3
Evaluate f(Xi) and rank Xi leading f(X1)
