1. Introduction
The metaheuristic optimization techniques have gained significant attention of researchers due to successful application of these techniques in a variety of complex optimization problems. These techniques are found more effective than conventional methods which use derivative information of function [1]. Two eminent features of any metaheuristic technique are exploration and exploitation. Exploration phase of algorithm, also known as diversification, redirects the search towards unvisited regions of the search space, in order to find new but potentially better solutions. On the other hand, exploitation or intensification phase helps the algorithm to search in the neighborhood of current best solutions. There are distinct objectives behind the development of modern metaheuristics such as fast and effortless handling of complex as well as large problems and designing more effective and robust techniques. New simple, easy to implement and powerful nature-inspired optimization algorithm called as owl search algorithm (OSA). This algorithm simulates the hunting mechanism of barn owls which rely on their hearing capability to find prey (vole) in the dark night rather than sight. The effectiveness of OSA is validated on a set of unconstrained numerical benchmark functions [2]. The results obtained are compared with standard optimizers like GA, PSO, BA, FFA, MVO, and KH. Further suitability of proposed technique, to resolve real world black box optimization problems, is investigated by conducting a real-time experimental study on Heat Flow Experiment (HFE) setup. These techniques are found more effective than conventional methods which use derivative information of function. Two eminent features of any metaheuristic technique are exploration and exploitation. Exploration phase of algorithm, also known as diversification, redirects the search towards unvisited regions of the search space, in order to find new but potentially better solutions [3]. A new simple, easy to implement and powerful nature-inspired optimization algorithm called as owl search algorithm (OSA).
2. Inspiration of Owl Search Algorithm (OSA)
Owls are typically nocturnal but highly efficient predators with an extraordinary auditory system which helps to locate the prey (vole). Some of the species like barn owls have evolved with a distinct anatomical feature of auditory system with vertical asymmetry of ears. Due to this unique feature, the sound reaches one ear before the other, and location of prey is obtained. Hence prey can be located in dark by hearing ability instead of sight [4]. The sound signal generated by a vole is processed in the owl’s brain in two parts i.e. the interaural time difference (ITD), and interaural level (loudness) difference (ILD) to prepare an auditory map of prey location. The distance of prey is estimated on the basis of time and intensity differences of sound wave arrival.
The effectiveness of OSA is validated on a set of unconstrained numerical benchmark functions. The results obtained are compared with standard optimizers like GA, PSO, BA, FFA, MVO, and KH. Further suitability of proposed technique, to resolve real world black box optimization problems, is investigated by conducting a real-time experimental study on Heat Flow Experiment (HFE) setup.
3. Owl Search Algorithm (OSA)
Similar to other nature-inspired population based algorithms, OSA starts the optimization process with an initial set of random solutions which represent the initial position of owls in a forest. To experimental studies are carried out to examine the efficiency, effectiveness and stability of OSA [5]. (OSA) and analyzes its potential in terms of its time complexity and the signaling involved. Instead of performing a search in a completely random manner, or assuming knowledge of the relative position of the destinations, OSA distributive constructs an approximated minimum-depth spanning tree. Then OSA searches this tree efficiently such that the number of search messages is minimized and the resulting path is of reasonable length. The only requirement of this search is two hop neighborhood information. We demonstrate that, given some neighborhood information, a minimum-depth spanning tree can be approximated, and that this approximation converges to the actual minimum-depth spanning tree as the size of the known neighborhood increases [6]. With the ordered walk search algorithm (OSA), we aim to take advantage of the smaller time complexity of BFS and combine it with the low communication complexity of DFS to further improve the efficiency of the search through the use of known topology (i.e., path) information. The basic idea is to approximate the construction of a minimum-depth spanning tree rooted at the source (as in BFS) and then performing DFS on this tree. If there is information about the past location of the destination, then this can be used to guide the search. We call this an informed search, and while such information can help the search, it not necessary for the OSA.
It is often the case in the establishment of new paths that no information about the destination is available at the node making a search decision. To make the search efficient, nodes can take advantage of topology information they have about their neighborhood. In particular, the number of nodes covered (in the known neighborhood) needs to be maximized while the number of nodes relaying the query needs to be minimized by the choice of next hop in the search [7]. To achieve this, successive nodes in the search should have as few neighbors in common as possible. This is possible if two-hop neighborhood topology information is known by performing set comparisons between the current search node and its neighbors. This would usually favor choosing nodes physically far apart, rather than close together, and consequently results in shorter paths than a random walk, where a near node and a far node have the same probability of being the successor in the search. While set comparisons can be computationally intensive, it should not be an issue with modern technology [8].
3.1. Steps for OSA
- Automated inconsistency detection
- Reasoning efficiency
- Scalability
- Expressivity
- Debugging aids
3.1.1. Automated inconsistency detection
Different feature requirements may be contradictory and the product configuration may be invalid respecting to the feature model. In order to prevent inconsistent products being combined from incompatible features, it is important that inconsistencies can be detected automatically. It allows the domain experts to focus only on the system to be built, rather than the usability of the tool [9]. Furthermore, the automation also enables the computer agents to compose software products run-timely based on users demands.
3.1.2. Reasoning efficiency
As a feature model may evolve constantly, specially for the dynamic re-configured feature systems, it requires the feature reasoning tool be able to conclude the validity of configurations in very short time.
3.1.3. Scalability
Modern software could be very large. Applications like Microsoft Windows OS have thousands of different features [10]. The manual checking of such models/configurations are highly painstaking and error-prone. Hence, the feature reasoning system should scale up well to handle large and complex models.
3.1.4. Expressivity
As features interact with each other, the relationship among various features could be very complicated [11]. The reasoning system should provide for means for representing and efficient reasoning over the wide variety of feature relations.
3.1.5. Debugging aids
It should provide some explanation as to why the feature models are inconsistent. The Semantic Web has emerged as the next generation of the Web since the past few years. Ontology languages such as OWL play a key role in realizing the full potential of the Semantic Web as they prescribe how data are defined and related [12]. According to W3C, “ontology defines the terms used to describe and represent an area of knowledge. Ontology includes computer-usable definitions of basic concepts in the domain and the relationships among them. They encode knowledge in a domain and also knowledge that spans domains. In this way, they make that knowledge reusable”. One of the advantages of logic based ontology languages, such as OWL, in particular OWL-DL or OWL-Lite, is that reasoners can be used to compute subsumption relationships between classes and to identify unsatisfiable (inconsistent) classes [13].
3.2. Flow Chart of OSA
4. Numerical Methods of OSA
In several cases the optimal solution vector and corresponding global solution are known only as a numerical approximation [14].
5. Applications of OSA
- Performance profile and Test problems
- Business component [15]
- Feature modeling
- Scanner [16]
- Web service technology [17]
- Hypothesis Testing (HyQue) [18]
6. Advantages of OSA
- It provides an easily accessible collection of standard test problems for continuous global optimization [19].
- The increasing availability of web services demands for a discovery mechanism to find services that satisfy our requirement [20].
- Feature models are widely used in domain engineering to capture common and variant features among systems in a particular domain [21].
- Its goal is to achieve a vibrant interchange between researchers and practitioners on fundamental and advanced issues related to intelligent systems [22].
- This yields a description that is open and extensible, and facilitates the sharing of design among software engineers [23].
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