Nature-inspired Heuristic Algorithm Research Articles

Application of Nature-Inspired Algorithms to Computed Tomography with Incomplete Data

This paper discusses and compares several computed tomography (CT) algorithms capable of dealing with incomplete data. This type of problem has been proposed for a symmetrical grid and symmetrically distributed transmitters and receivers. The use of symmetry significantly speeds up the process of constructing a system of equations that is the foundation of all CT algebraic algorithms. Classic algebraic approaches are effective in incomplete data scenarios, but suffer from low convergence speed. For this reason, we propose the use of nature-inspired algorithms which are proven to be effective in many practical optimization problems from various domains. The efficacy of nature-inspired algorithms strongly depends on the number of parameters they maintain and reproduce, and this number is usually substantial in the case of CT applications. However, taking into account the specificity of the reconstructed object allows to reduce the number of parameters and effectively use heuristic algorithms in the field of CT. This paper compares the efficacy and suitability of three nature-inspired heuristic algorithms: Artificial BeeColony (ABC), Ant Colony Optimization (ACO), and Clonal Selection Algorithm (CSA) in the CT context, showing their advantages and weaknesses. The best algorithm is identified and some ideas of how the remaining methods could be improved so as to better solve CT tasks are presented.

Optimal allocation of solar photovoltaic distributed generation in electrical distribution networks using Archimedes optimization algorithm

Abstract This paper proposes to resolve optimal solar photovoltaic (SPV) system locations and sizes in electrical distribution networks using a novel Archimedes optimization algorithm (AOA) inspired by physical principles in order to minimize network dependence and greenhouse gas (GHG) emissions to the greatest extent possible. Loss sensitivity factors are used to predefine the search space for sites, and AOA is used to identify the optimal locations and sizes of SPV systems for reducing grid dependence and GHG emissions from conventional power plants. Experiments with composite agriculture loads on a practical Indian 22-bus agricultural feeder, a 28-bus rural feeder and an IEEE 85-bus feeder demonstrated the critical nature of optimally distributed SPV systems for minimizing grid reliance and reducing GHG emissions from conventional energy sources. Additionally, the voltage profile of the network has been enhanced, resulting in significant reductions in distribution losses. The results of AOA were compared to those of several other nature-inspired heuristic algorithms previously published in the literature, and it was observed that AOA outperformed them in terms of convergence and redundancy when solving complex, non-linear and multivariable optimization problems. Optimal solar photovoltaic system locations and sizes in electrical distribution networks are derived using a novel Archimedes optimization algorithm in order to minimize network dependence and pollutant emissions to the greatest extent possible.

An Improved Flower Pollination Algorithm for Global and Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.

Community Structure Detection Using Firefly Algorithm

This article describes how parallel to the continuous growth of the Internet, which allows people to share and collaborate more, social networks have become more attractive as a research topic in many different disciplines. Community structures are established upon interactions between people. Detection of these communities has become a popular topic in computer science. How to detect the communities is of great importance for understanding the organization and function of networks. Community detection is considered a variant of the graph partitioning problem which is NP-hard. In this article, the Firefly algorithm is used as an optimization algorithm to solve the community detection problem by maximizing the modularity measure. Firefly algorithm is a new Nature-inspired heuristic algorithm that proved its good performance in a variety of applications. Experimental results obtained from tests on real-life networks demonstrate that the authors' algorithm successfully detects the community structure.

Realistic Scheduling Mechanism for Smart Homes

In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.

Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.

Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.

A novel hybrid multi-objective immune algorithm with adaptive differential evolution

In this paper, we propose a novel hybrid multi-objective immune algorithm with adaptive differential evolution, named ADE-MOIA, in which the introduction of differential evolution (DE) into multi-objective immune algorithm (MOIA) combines their respective advantages and thus enhances the robustness to solve various kinds of MOPs. In ADE-MOIA, in order to effectively cooperate DE with MOIA, we present a novel adaptive DE operator, which includes a suitable parent selection strategy and a novel adaptive parameter control approach. When performing DE operation, two parents are respectively picked from the current evolved and dominated population in order to provide a correct evolutionary direction. Moreover, based on the evolutionary progress and the success rate of offspring, the crossover rate and scaling factor in DE operator are adaptively varied for each individual. The proposed adaptive DE operator is able to improve both of the convergence speed and population diversity, which are validated by the experimental studies. When comparing ADE-MOIA with several nature-inspired heuristic algorithms, such as NSGA-II, SPEA2, AbYSS, MOEA/D-DE, MIMO and D2MOPSO, simulations show that ADE-MOIA performs better on most of 21 well-known benchmark problems.

Efficiency Improvements of Antenna Optimization Using Orthogonal Fractional Experiments

This paper presents an extremely efficient method for antenna design and optimization. Traditionally, antenna optimization relies on nature-inspired heuristic algorithms, which are time-consuming due to their blind-search nature. In contrast, design of experiments (DOE) uses a completely different framework from heuristic algorithms, reducing the design cycle by formulating the surrogates of a design problem. However, the number of required simulations grows exponentially if a full factorial design is used. In this paper, a much more efficient technique is presented to achieve substantial time savings. By using orthogonal fractional experiments, only a small subset of the full factorial design is required, yet the resultant response surface models are still effective. The capability of orthogonal fractional experiments is demonstrated through three examples, including two tag antennas for radio-frequency identification (RFID) applications and one internal antenna for long-term-evolution (LTE) handheld devices. In these examples, orthogonal fractional experiments greatly improve the efficiency of DOE, thereby facilitating the antenna design with less simulation runs.