Optics Inspired Optimization Research Articles

An innovative machine learning based on feed-forward artificial neural network and equilibrium optimization for predicting solar irradiance

As is known, having a reliable analysis of energy sources is an important task toward sustainable development. Solar energy is one of the most advantageous types of renewable energy. Compared to fossil fuels, it is cleaner, freely available, and can be directly exploited for electricity. Therefore, this study is concerned with suggesting novel hybrid models for improving the forecast of Solar Irradiance (IS). First, a predictive model, namely Feed-Forward Artificial Neural Network (FFANN) forms the non-linear contribution between the IS and dominant meteorological and temporal parameters (including humidity, temperature, pressure, cloud coverage, speed and direction of wind, month, day, and hour). Then, this framework is optimized using several metaheuristic algorithms to create hybrid models for predicting the IS. According to the accuracy assessments, metaheuristic algorithms attained satisfying training for the FFANN by using 80% of the data. Moreover, applying the trained models to the remaining 20% proved their high proficiency in forecasting the IS in unseen environmental circumstances. A comparison among the optimizers revealed that Equilibrium Optimization (EO) could achieve a higher accuracy than Wind-Driven Optimization (WDO), Optics Inspired Optimization (OIO), and Social Spider Algorithm (SOSA). In another phase of this study, Principal Component Analysis (PCA) is applied to identify the most contributive meteorological and temporal factors. The PCA results can be used to optimize the problem dimension, as well as to suggest effective real-world measures for improving solar energy production. Lastly, the EO-based solution is yielded in the form of an explicit formula for a more convenient estimation of the IS.

Optics-based metaheuristic approach to assess critical failure surfaces in both circular and non-circular failure modes for slope stability analysis

In many construction projects, a proactive slope stability evaluation is a prerequisite. Although many deterministic or non-deterministic approaches have been commonly used, metaheuristic approaches have resulted in high precision and significant outcomes for slope stability analysis problems. The current work focuses on the reliable assessment of critical failure surfaces associated with the least factor of safety value in both homogeneous and non-homogeneous slopes using a new simplified meta-heuristic approach called optics-inspired optimization (OIO). The algorithm utilizes six different LEM methods as a fitness function for deriving the factor of safety. Experimental analysis over three benchmark studies has been performed to demonstrate the algorithm's robustness and effectiveness. The implementation found more robust results as compared to previous studies. Meanwhile, the algorithm's statistical implication is conducted using the ANOVA test, which inferred better outcomes. With this interpretation, the approach claims to be suitable and efficient for slope stability analysis.

Chaos enhanced intelligent optimization-based novel deception detection system

Today, with the developing technology, access methods to information have also changed. Internet blogs and news sites, social media, etc. replace the traditional information access tools such as TV, radio, newspaper, and magazines. Cheaper and faster access than traditional methods and easy access from anywhere where the internet is available are the main factors in changing the access method to information. Besides, information spreads rapidly on the internet without proving the accuracy. There may be many reasons for the distribution of misinformation in this way, such as commercial, political, and economic. Information with fake content and deception purposes negatively affects both the person and the society. It is extremely important to detect deceptive information in textual data and researchers continue to propose new models in order to obtain efficient results in terms of many metrics. This paper proposes a new approach for deception detection problems. The deception detection problem was considered an optimization problem for the first time. Optics Inspired Optimization (OIO), Grey Wolf Optimization (GWO), and Chaos Based Optics Inspired Optimization (CBOIOs) are adapted and modeled for the first time in the deception detection problem. Deception detection models proposed in this article include data preprocessing, adapting optimization methods, and testing stages. An experimental evaluation and comparison of the proposed model and seven supervised machine learning algorithms are performed within two different data sets depending on four evaluation metrics (Accuracy, Recall, Precision, and F-Measure). Results show that CBOIOs are more effective than OIO, GWO, and other machine learning algorithms for this problem.

A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings

Recent studies have witnessed remarkable merits of metaheuristic algorithms in optimization problems. Due to the significance of the early analysis of the thermal load in energy-efficient buildings, this work introduces and compares four novel optimizer techniques—the firefly algorithm (FA), optics-inspired optimization (OIO), shuffled complex evolution (SCE), and teaching–learning-based optimization (TLBO)—for an accurate prediction of the heating load (HL). The models are applied to a multilayer perceptron (MLP) neural network to surmount its computational shortcomings. The models are fed by a literature-based dataset obtained for residential buildings. The results revealed that all models used are capable of properly analyzing and predicting the HL pattern. A comparison between them, however, showed that the TLBO-MLP with the coefficients of determination 0.9610 vs. 0.9438, 0.9373, and 0.9556 (respectively, for FA-MLP, OIO-MLP, and SCE-MLP) and the root mean square error of 2.1103 vs. 2.5456, 2.7099, and 2.2774 presents the most reliable approximation of the HL. It also surpassed several methods used in previous studies. Thus, the developed TLBO-MLP can be a beneficial model for subsequent practical applications.

New modified algorithm: θ-turbulent flow of water-based optimization.

The reactive power control of a power system is discussed under two types of variables: continuous variables (e.g., generator bus voltages) and discrete variables (e.g., transformer taps and the size of switched shunt capacitors). This paper proposes a novel and powerful algorithm, named turbulent flow of water-based optimization (TFWO) as well as a new improved version of this algorithm, called θ-TFWO, for optimal reactive power distribution (ORPD) to reduce losses. The proposed method is applied to two large-scale IEEE 57-bus systems. Furthermore, to demonstrate the competitive performance of the suggested algorithm, its performance was compared to that of several other algorithms, including biogeography-based optimization (BBO), social spider algorithm (SSA), and optics inspired optimization (OIO), in terms of solving the ORPD problem. The results confirmed the robustness and effectiveness of the proposed method as a powerful optimizer applicable to optimal reactive power distribution in power systems.

Chaos based optics inspired optimization algorithms as global solution search approach

Abstract Metaheuristic optimization algorithms are efficiently used in many large-scale complex problems. Recently, a physics-based metaheuristic search and optimization method entitled Optics Inspired Optimization (OIO) has been proposed. OIO treats the search field of the interested problem to be optimized as a wavy mirror in which the concave mirror is represented as a valley and the convex mirror is represented as a peak. Each candidate solution represents an artificial light point. OIO is a very new metaheuristic method and different approaches should be integrated to obtain a faster convergence with high accuracy by balancing the exploitation and exploration. This paper is the first work on performance improvement of this method by preventing the falling into local optimum solutions and slow convergence speed. In this article, different ergodic chaotic systems are used for the first time to generate chaotic values instead of random values in OIO processes in order to enhance the global convergence speed and prevent stuck on local solutions of classical OIO algorithm. For this purpose, three new enhanced OIO methods are proposed. Furthermore, a new application area for chaos is proposed. The chaotic OIO algorithms proposed in this study are tested in unconstrained benchmark problems and constrained real-world engineering problems. Promising results are obtained from the detailed simulations.

Optimal reactive power dispatch using θ-social mimic optimization (θ-SMO)

The social mimic optimization algorithm (SMO) and its enhanced version (θ-SMO) is presented in the current study for the optimal dispatch problem of the reactive power (ORPD) with continuous and discrete control variables in the IEEE standard networks. The feasibleness and functioning of the θ-SMO and SMO algorithms are indicated for the IEEE 57-bus, and IEEE 118-bus standard networks. The outcomes of the simulation were compared, and it was shown that the optimization efficacy of these algorithms is higher than other rooted algorithms, such as optics in-spired optimization (OIO), the social spider algorithm (SSA) algorithm, and biogeography-based optimization (BBO). Results obtained for ORPD problem indicate better performance concerning the θ-SMO algorithm’s solution quality compared to original SMO algorithm and other algorithms.

A Physics Based Novel Approach for Travelling Tournament Problem: Optics Inspired Optimization

Computational intelligence search and optimization algorithms have been efficiently adopted and used for many types of complex problems. Optics Inspired Optimization (OIO) is one of the most recent physics inspired computational intelligence methods which treats the search space of the problem to be optimized as a wavy mirror in which each peak is assumed to reflect as a convex mirror and each valley to reflect as a concave one. Each candidate solution is treated as an artificial light point that its glittered ray is reflected back by the search space of the problem and the artificial image is formed based on mirror equations adopted from Optics, as a new candidate solution. In this study, OIO for the first time has been designed as solution search strategy for travelling tournament problem which is one of the current sports problems and aids to minimize transportation and total movement of teams. Furthermore, this problem has been firstly solved by League Championship Algorithm and obtained results from both synthetic and real datasets have been compared in this study for the first time. Obtained results show the superiority of OIO which is a novel algorithm and seems to efficiently solve many complex problems.

Enhanced grouping league championship and optics inspired optimization algorithms for scheduling a batch processing machine with job conflicts and non-identical job sizes

This research aims to address scheduling of a single batch processing machine, where jobs are in different sizes and have a conflicting nature with each other, in the sense that two conflicting jobs cannot share the same batch and hence, cannot be processed simultaneously by the machine. The problem is referred to as SBMC. After formulating the problem, a strong lower bound procedure is developed by transforming a relaxed version of the scheduling problem to the multiple bottleneck transportation problem (MTP) with conflicts. An efficient Lagrangian relaxation procedure is proposed, which takes advantage of decomposable nature of the relaxed problem, to attain a lower bound for MTP which in essence is a lower bound for SBMC. To solve the problem, two metaheuristic algorithms, namely the League Championship Algorithm (LCA) and Optic Inspired Optimization (OIO), are adapted and fundamentally modified to match the problem unique structure (i.e., the grouping structure) and accordingly the grouping version of these algorithms is developed. The effectiveness of the lower bound procedure, as well as algorithms, are evaluated through extensive computational experiments. To show they perform efficiently in running times and effective in finding near-optimal bounds for most of the problem instances, we generate 20 different classes of problems according to variability in job sizes, number of jobs and density of incompatibility matrix. Then, we make comparison with two of extensively used algorithms from literature, SA and GA. Our proposed algorithms obtain solutions with objective values less than 6% gap from the lower bound and outperform SA and GA algorithms, especially for large instances. Moreover, values of the lower bound procedure lie within less than 4% of objective values provided by CPLEX.

Social mimic optimization algorithm and engineering applications

Increase in complexity of real world problems has provided an area to explore efficient methods to solve computer science problems. Meta-heuristic methods based on evolutionary computations and swarm intelligence are instances of techniques inspired by nature. This paper presents a novel social mimic optimization (SMO) algorithm inspired by mimicking behavior to solve optimization problems. The proposed algorithm is evaluated using 23 test functions. Obtained results are compared with 14 known optimization algorithms including Whale optimization algorithm (WOA), Grasshopper optimization algorithm (GOA), Particle Swarm Optimization (PSO), Stochastic fractal search (SFS), Grey Wolf Optimizer (GWO), Optics Inspired Optimization (OIO), League Championship Algorithm (LCA), Wind Driven Optimization (WDO), Harmony search (HS), Firefly Algorithm (FA), Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), Bat Algorithm (BA), and Teaching Learning Based Optimization (TLBO). Obtained results indicate higher capability of the SMO algorithm in solving high-dimensional decision variables. Furthermore, SMO is used to solve two classic engineering design problems. Three important features of SMO are simple implementation, solving optimization problems with minimum population size and not requiring control parameters. Results of various evaluations show superiority of the proposed method in finding the optimal solution with minimum function evaluations. This superiority is achieved based on reducing number of initial population. The proposed method can be applied to applications like automatic evolution of robotics, automatic control of machines and innovation of machines in finding better solutions with less cost.

An optics inspired optimization method for optimal design of truss structures

SummaryThis paper presents optics inspired optimization (OIO) as a new optimization technique for optimum design of truss structures with continuous variables. In OIO, the physical laws that govern the image formation in spherical mirrors are artificially modeled to develop a new metaheuristic method. In OIO, the surface of the objective function to be minimized is considered as the reflecting wavy mirror consisting of peaks and valleys. In the reflecting surface, each peak is assumed to reflect as a convex mirror and each valley to reflect as a concave one. By assuming each solution candidate as an artificial object (or light point) that is artificially glittered ray is reflected back by the function surface, the artificial image (new solution) is formed based on the governing equations in physics of optics. Compared with the other existing optimization techniques, OIO seems to be a promising alternative approach for solving truss optimum design problems.

Optimum discrete design of steel tower structures using optics inspired optimization method

SummaryThis paper presents an optics inspired optimization (OIO) method for optimum design of steel tower structures with discrete variables. Inspired by the optical characteristics of concave and convex mirrors, OIO tries to solve optimization problem. In OIO, the surface of the objective function to be minimized is considered as a reflecting wavy mirror consisting of peaks and valleys. To generate a new solution (artificial image point) from a given solution (artificial object/light point) in the search space, it is assumed that the artificial ray glittered from the artificial light point is reflected back artificially by the function surface in which each peak is considered as a convex mirror and each valleys is treated as a concave mirror. Then, the artificial image point is formed by the theory of optics in Physics, and a new solution is generated in the search space accordingly. Numerical experiments have been conducted on 4 benchmark design examples with discrete variables, and the results obtained by OIO are compared to those reported in the literature. The results show that OIO can produce high quality solutions and show a relatively fast convergence rate.

Comparative Performance Analysis of Optimal PID Parameters Tuning Based on the Optics Inspired Optimization Methods for Automatic Generation Control

The Optics Inspired Optimization (OIO) algorithm is a new metaheuristic optimization method. In this paper, the OIO algorithm was proposed for automatic production control parameters in electrical power systems. The performance of the proposed algorithm was realized on two power systems that have different structures. The first structure is a two-area interconnected thermal reheat power system and the other one is a two-area interconnected multi-unit hydro-thermal power system. The results obtained with the proposed algorithm were compared with an artificial bee colony and particle swarm optimization, initial values are randomly defined that are commonly used in literature. The results were examined using four different cost functions based on area control error. Considering the obtained results, the proposed algorithm reached to the global minimum value with less number of iterations and is more suitable for online optimization. According to the results obtained with this novel method, it has a better performance for maximum overshoot and settling time values when the test systems are implemented.

CRWO: Clustering and routing in wireless sensor networks using optics inspired optimization

In wireless sensor networks (WSNs), cluster head (CH) selection and routing are two well known optimization problems associated with high computational complexity for large scale networks. Optics inspired optimization (OIO) is one of the recent meta-heuristic techniques used to solve a wide range of NP-Hard problems. In this study, firstly an OIO based CH selection algorithm is proposed by considering various parameters such as energy, distance and node degree. Next, clusters are formed using derived potential function. Finally, an OIO based routing algorithm is proposed to compute the route from each CH to the base station (BS) using the same parameters like energy, distance and node degree. In the performance evaluation, the proposed algorithm (CRWO) is extensively tested and compared with some of the existing algorithms based on both conventional and nature inspired routing techniques. Simulation results depict the superiority of the proposed algorithm over its comparatives.

An effective algorithm for constrained optimization based on optics inspired optimization (OIO)

Due to the law of reflection, the converging and diverging behavior of concave and convex mirrors causes that curved mirrors show different image types. The optics inspired optimization (OIO) is a recently proposed algorithm for unconstrained optimization which treats the surface of the function to be optimized as a wavy mirror in which each peak is assumed to reflect as a convex mirror and each valley to reflect as a concave one. Each individual is treated as an artificial light point that its glittered ray is reflected back by the function surface, given that the surface is convex or concave, and the artificial image (a new solution) is formed based on mirror equations adopted from Optics. There are several constraint handling techniques which have been proposed for handling infeasible solutions. However, these techniques may suffer from problem dependency, no unique way for designing their operators, no unique way for updating their internal parameters, increasing the computational complexity, etc. To equip OIO with a mechanism to handle constraints and to avoid the drawbacks of typical techniques, a feasibility measure is used beside the objective function value to bias the search toward feasible regions. Such a consideration requires to modify several modules in the basic OIO algorithm. To increase the probability to generate better solutions, a number of alternative solutions are produced from each individual and one is selected based on the sequential use of modified Deb’s tournament selection. Besides, Deb’s tournament selection rule is used in place of the greedy selection in basic OIO, along with allowing the survival of individuals with a good value of the objective function, regardless of their feasibility. Performance of the proposed algorithm is compared with a number of noticeable algorithms such as COPSO, ECHT-EP2, αSimplex etc, on CEC 2006 and CEC 2010 set of benchmark problems and on a set of mechanical design optimization problems. Results demonstrate that the proposed algorithm performs the global optimization task very well and competitive. Such an outcome encourages that further developments and applications of OIO would be worth to realize its full potency in the future studies.

A new metaheuristic for optimization: Optics inspired optimization (OIO)

Due to the law of reflection, a concave reflecting surface/mirror causes the incident light rays to converge and a convex surface/mirror causes the light rays to reflect away so that they all appear to be diverging. These converging and diverging behaviors cause that the curved mirrors show different image types depending on the distance between the object and the mirror. We model such optical phenomena metaphorically into the searching process of numerical optimization by a new algorithm called optics inspired optimization (OIO). OIO treats the surface of the numerical function to be optimized as a reflecting surface in which each peak is assumed to reflect as a convex mirror and each valley to reflect as a concave one. Each individual is assumed to be an artificial object (or light point) that its artificially glittered ray is reflected back by the function surface, given that the surface is convex or concave, and the artificial image is formed (a candidate solution is generated within the search domain) based on the mirror equations adopted from physics of optics. Besides OIO, we introduce different variants of it, called ROIO (Rotation based OIO), and COIO (Convex combination based OIO) algorithms and conduct an extensive computational effort to find out the merit of the new algorithms. Our comparisons on benchmark test functions and a real world engineering design application (i.e., optimization of a centrifuge pump) demonstrate that the new algorithms are efficient and compete better than or similar to most of state of the art optimization algorithms with the advantage of accepting few input parameters.