Urvinder Singh

Modified Grey Wolf Optimizer for Global Engineering Optimization

Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer GWO, which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO mGWO is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.

Design of Linear and Circular Antenna Arrays Using Cuckoo Optimization Algorithm

Cuckoo optimization Algorithm (COA) is employed for the optimization of linear and non- uniform circular antenna arrays. COA is a novel nature inspired computing algorithm which is motivated by the life of Cuckoo. Like other nature-inspired algorithms, COA is also a population-based method and uses a population of solutions to proceed to the global solution. The method of COA is used to determine a set of parameters of antenna elements that provide the required radiation pattern. The efiectiveness of COA for the design of antenna arrays is shown by means of numerical results. Comparison of results of COA is made with that obtained using other popular methods. The results reveal the superior performance of COA as compared to other techniques both for design of linear and circular antenna arrays.

Application of mutation operators to flower pollination algorithm

A new concept based on mutation operators is applied to flower pollination algorithm (FPA).Based on mutation, five new variants of FPA are proposed.Dynamic switch probability is used in all the proposed variants.Benchmarking of Variants with respect to standard FPA.Benchmarking and statistical testing of the best variant with respect to state-of-the-art algorithms. Flower pollination algorithm (FPA) is a recent addition to the field of nature inspired computing. The algorithm has been inspired from the pollination process in flowers and has been applied to a large spectra of optimization problems. But it has certain drawbacks which prevents its applications as a standard algorithm. This paper proposes new variants of FPA employing new mutation operators, dynamic switching and improved local search. A comprehensive comparison of proposed algorithms has been done for different population sizes for optimizing seventeen benchmark problems. The best variant among these is adaptive-Lvy flower pollination algorithm (ALFPA) which has been further compared with the well-known algorithms like artificial bee colony (ABC), differential evolution (DE), firefly algorithm (FA), bat algorithm (BA) and grey wolf optimizer (GWO). Numerical results show that ALFPA gives superior performance for standard benchmark functions. The algorithm has also been subjected to statistical tests and again the performance is better than the other algorithms.

A stable energy efficient clustering protocol for wireless sensor networks

Sensor networks comprise of sensor nodes with limited battery power that are deployed at different geographical locations to monitor physical events. Information gathering is a typical but an important operation in many applications of wireless sensor networks (WSNs). It is necessary to operate the sensor network for longer period of time in an energy efficient manner for gathering information. One of the popular WSN protocol, named low energy adaptive clustering hierarchy (LEACH) and its variants, aim to prolong the network lifetime using energy efficient clustering approach. These protocols increase the network lifetime at the expense of reduced stability period (the time span before the first node dies). The reduction in stability period is because of the high energy variance of nodes. Stability period is an essential aspect to preserve coverage properties of the network. Higher is the stability period, more reliable is the network. Higher energy variance of nodes leads to load unbalancing among nodes and therefore lowers the stability period. Hence, it is perpetually attractive to design clustering algorithms that provides higher stability, lower energy variance and are energy efficient. In this paper to overcome the shortcomings of existing clustering protocols, a protocol named stable energy efficient clustering protocol is proposed. It balances the load among nodes using energy-aware heuristics and hence ensures higher stability period. The results demonstrate that the proposed protocol significantly outperforms LEACH and its variants in terms of energy variance and stability period.

Synthesis of linear antenna array using flower pollination algorithm

Linear antenna array (LAA) design is a classical electromagnetic problem. It has been extensively dealt by number of researchers in the past, and different optimization algorithms have been applied for the synthesis of LAA. This paper presents a relatively new optimization technique, namely flower pollination algorithm (FPA) for the design of LAA for reducing the maximum side lobe level (SLL) and null control. The desired antenna is achieved by controlling only amplitudes or positions of the array elements. FPA is a novel meta-heuristic optimization method based on the process of pollination of flowers. The effectiveness and capability of FPA have been proved by taking difficult instances of antenna array design with single and multiple objectives. It is found that FPA is able to provide SLL reduction and steering the nulls in the undesired interference directions. Numerical results of FPA are also compared with the available results in the literature of state-of-the-art algorithms like genetic algorithm, particle swarm optimization, cuckoo search, tabu search, biogeography based optimization (BBO) and others which also proves the better performance of the proposed method. Moreover, FPA is more consistent in giving optimum results as compared to BBO method reported recently in the literature.

A novel bat flower pollination algorithm for synthesis of linear antenna arrays

In this paper, a novel algorithm, namely bat flower pollination (BFP) is proposed for synthesis of unequally spaced linear antenna array (LAA). The new method is a combination of bat algorithm (BA) and flower pollination algorithm (FPA). In BFP, both BA and FPA interact with each other to escape from local minima. The results of BFP for solving a set of 13 benchmark functions demonstrate its superior performance as compared to variety of well-known algorithms available in the literature. The novel proposed method is also used for the synthesis of unequally spaced LAA for single and multi-objective design. Simulation results show that BFP is able to provide better synthesis results than wide range of popular techniques like genetic algorithm, differential evolution, cuckoo search, particle swarm optimization, back scattering algorithm and others.

A Novel Binary Spider Monkey Optimization Algorithm for Thinning of Concentric Circular Antenna Arrays

ABSTRACT This paper presents a novel binary algorithm named as binary spider monkey optimization (binSMO) for thinning of concentric circular antenna arrays (CCAA). The proposed algorithm has been adapted from a recently developed nature inspired optimization method, spider monkey optimization (SMO). SMO works in continuous domain and as such is not suitable for application to binary optimization problems. The binSMO algorithm has been proposed with inclusion of logical operators in SMO for binary thinning problem. Thinning of an antenna array reduces the maximum side lobe level (SLL) as well as cost and size of antenna array. Thinning of CCAA can be modelled as 0–1 binary integer optimization problem. The proposed binSMO is used to synthesize CCAA in order to reduce the SLL and at the same time keeping the percentage of thinning equal to or more than the desired level. Simulation examples of two ring and ten ring CCAA have been considered. The novel method binSMO gives reduced SLL as compared to the results available in literature of teacher learning based optimization, biogeography based optimization, modified particle swarm optimization, and firefly algorithm. Moreover, the convergence rate of binSMO is faster than the other methods. The results prove the competence and superiority of binSMO to existing metaheuristic algorithms and it has an ability to become an effective tool for solving binary optimization problems.

Pattern Synthesis of Linear Antenna Arrays Using Enhanced Flower Pollination Algorithm

In this paper, a new variant of flower pollination algorithm (FPA), namely, enhanced flower pollination algorithm (EFPA), has been proposed for the pattern synthesis of nonuniform linear antenna arrays (LAA). The proposed algorithm uses the concept of Cauchy mutation in global pollination and enhanced local search to improve the exploration and exploitation tendencies of FPA. It also uses dynamic switching to control the rate of exploration and exploitation. The algorithm is tested on standard benchmark problems and has been compared statistically with state of the art to prove its worthiness. LAA design is a tricky and difficult electromagnetic problem. Hence to check the efficacy of the proposed algorithm it has been used for synthesis of four different LAA with different sizes. Experimental results show that EFPA algorithm provides enhanced performance in terms of side lobe suppression and null control compared to FPA and other popular algorithms.

A boolean spider monkey optimization based energy efficient clustering approach for WSNs

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

New cuckoo search algorithms with enhanced exploration and exploitation properties

Abstract Cuckoo Search (CS) algorithm is nature inspired global optimization algorithm based on the brood parasitic behavior of cuckoos. It has proved to be an efficient algorithm as it has been successfully applied to solve a large number of problems of different areas. CS employs Levy flights to generate step size and to search the solution space effectively. The local search is carried out using switch probability in which certain percentages of solutions are removed. Though CS is an effective algorithm, still its performance can be improved by incorporating the exploration and exploitation during the search process. In this work, three modified versions of CS are proposed to improve the properties of exploration and exploitation. All these versions employ Cauchy operator to generate the step size instead of Levy flights to efficiently explore the search space. Moreover, two new concepts, division of population and division of generations, are also introduced in CS so as to balance the exploration and exploitation. The proposed versions of CS are tested on 24 standard benchmark problems with different dimension sizes and varying population sizes and the effect of probability switch has been studied. Apart from this, the best of the proposed versions is also tested on CEC 2015 benchmark suite. The modified algorithms have been statistically tested in comparison to the state-of-the-art algorithms, namely grey wolf optimization (GWO), differential evolution (DE), firefly algorithm (FA), flower pollination algorithm (FPA) and bat algorithm (BA). The numerical and statistical results prove the superiority of the proposed versions with respect to other popular algorithms available in the literature.