SUAD MOHAMMED et al: IMPROVING THE EFFECTIVENESS OF THE BLACK HOLE ALGORITHM USING A . . DOI 10.5013/IJSSST.a.17.04.12 12.1 ISSN: 1473-804x online, 1473-8031 print Improving the Effectiveness of the Black Hole Algorithm using a Local Search Technique Suad Khairi Mohammed, Hamdan Daniyal, Norazian Subari, Badaruddin Muhammad, Zulkifli Musa Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang Pekan, Pahang, Malaysia suad.khairim@gmail.com, zuwairie@ump.edu.my, hamdan@ump.edu.my, aziansubari@ump.edu.my, badaruddinmuhammad@yahoo.com, zkifli@ump.edu.my Nor Azlina Ab. Aziz Faculty of Engineering and Technology Multimedia University Melaka, Malaysia azlina.aziz@mmu.edu.my Zuwairie Ibrahim, Kamil Zakwan Mohd Azmi Faculty of Manufacturing Engineering Universiti Malaysia Pahang Pekan, Pahang, Malaysia kamil_zakwan@yahoo.com.my Tasiransurini Ab Rahman Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor, Malaysia surini@uthm.edu.my Abstract - The main objective of the proposed algorithm in this paper is to modify the BH algorithm such that the simplicity can be resolved and improvements can be obtained in solving numerical optimization problems. The modified BH algorithm is presented by apply local search in BH algorithm to find neighborhood solution around the best solution. The black hole with local search (BHLS) algorithm is used as a new optimization approach to solve numerical optimization problems, specifically, unimodal, multimodal, hybrid, and composite functions of CEC2014 test suite. Keywords - Meta-heuristic, black hole, local search and numerical optimization problems. I. INTRODUCTION Almost all new meta- heuristics algorithms can be referred to the nature-inspired. The nature inspired algorithms lies in the fact that it receives its sole inspiration from nature. They have the ability to describe and resolve complex relationships from intrinsically very simple initial conditions and rules with little or no knowledge of the search space. Nature is the perfect example for optimization, because if we closely examine each and every features or phenomenon in nature it always find the optimal strategy. Some of these meta-heuristic algorithms were inspired by nature such as ant colony optimization (ACO) [1], bee colony optimization [2], particle swarm optimization (PSO) [3], gravitational search algorithm (GSA) [4], and black hole algorithm (BHA) [5] with additional features that allow them to explore the entire search space. Meta-heuristics are typically high-level strategies which guide an underlying, more problem specific heuristic, to increase their performance. The main goal is to avoid the disadvantages of iterative improvement and, in particular, multiple descents by allowing the local search to escape from local optima. This is achieved by either allowing worsening moves or generating new starting solutions for the local search in a more intelligent way than just providing random initial solutions. These algorithms are stochastic and approximate. They are stochastic in the sense that they try different random solutions through the search, and they are approximate since they try only a subset of the search space. Thus, the-meta-heuristic algorithms [6] cannot guarantee an optimal solution, but in most cases they result in a near optimal solution. Every meta- heuristic algorithm has mainly two components. First one is exploration and other one is exploitation. Exploration is the process of visiting entirely new regions of a search space,