Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems

In this paper, a new variant of Manta Ray Foraging Optimization (MRFO) algorithm is introduced to deal with real parameter constrained optimization problem. Gradient-based Mutation MRFO (GbM-MRFO) is derived from basic strategy of MRFO and synergized with the Gradient-based Mutation strategy. MRFO is a recently new introduced algorithm that consists of strategy of foraging adopted by Manta Ray while Gradient-based Mutation (GbM) is a feasibility-and solution repair strategy adopted from ϵ-Matrix-Adaptation Evolution Strategy (ϵ-MAES). MRFO is proven to solve artificial benchmark-function test by relatively good performance compared to several state-of-the-art algorithm while GbM is a productive approach to repair solution which led to improve the feasibility of the solution throughout the search by using Jacobian approximation in finite differences. GbM-MRFO turn out to be a competitive optimization algorithm on solving constrained optimization problem of Three-bar Truss problem. The performance of GbM-MRFO is proven to be efficient in solving the problems by providing lighter weight of truss with better accuracy of solution

Opposition-based spiral dynamic algorithm with an application to optimize type-2 fuzzy control for an inverted pendulum system

This paper presents two variants of the Opposition-based Spiral Dynamic Algorithm (ObSDA) for an application to optimize a type-2 fuzzy logic controller for an inverted pendulum system. Spiral Dynamic Algorithm (SDA) is a group-based optimization algorithm formulated based on the concept of a natural spiral phenomenon on earth. It has the theory of diversification and intensification in its strategy, which allows the algorithm to present itself as a good deterministic type of optimization tool to solve various engineering problems. Despite the good concept and strategy, the algorithm still suffers from getting trapped in a local optima solution. This is due to the limitation of the deterministic strategy that prevents the search agents from sufficiently exploring the whole feasible search space. The search operation only occurs within the area covered by the search agents, and thus there is a low opportunity to thoroughly diverse outside the covered area. Quasi-reflected and Quasi opposition-based strategies were incorporated into the SDA to overcome the exploration problem of the search agents. It helped the search agents to explore the opposite location of the current location of the agents. The opposition strategy also offered varying step sizes to the agents during the movement. The proposed QR-ObSDA and Q-ObSDA were tested on various benchmark functions comprising multimodal and unimodal fitness landscapes. They are also applied to optimize a type-2 fuzzy logic controller for an inverted pendulum system in comparison to SDA, Spotted Hyena Optimizer, Tunicate Swarm Algorithm, and Sooty Tern Optimization Algorithm. A statistical analysis on the accuracy achievement was conducted using Friedman and Wilcoxon Sign Rank methods. The result had shown that the proposed ObSDA variants had outperformed the original SDA in locating the theoretical optima solution of the benchmark functions. Application of the control problem had shown the accuracy performance of ObSDA variants had significantly improved compared to the existing SDA variants and outperformed the other three optimization algorithms

Super-opposition spiral dynamic-based fuzzy control for an inverted pendulum system

This paper presents a hybrid spiral dynamic algorithm with a super-opposition spiral dynamic algorithm (SOSDA) strategy. An improvement on the spiral dynamic algorithm (SDA), this method uses a concept centered on opposition-based learning, which is used to evaluate the fitness of agents at the opposite location to the current solution. The SDA is a simple-structured and deterministic type of algorithm, which also performs competitively in terms of solution accuracy. However, its deterministic characteristic means the SDA suffers premature convergence caused by the unbalanced diversification and intensification during its search procedure. Thus, the algorithm fails to achieve highly accurate solutions. It is proposed that adopting super-opposition into the SDA would enable the deterministic and random techniques to complement one another. The SOSDA was tested on four benchmark functions and compared to the original SDA. To analyze the result statistically, the Friedman and Wilcoxon tests were conducted. Furthermore, the SOSDA was applied to optimize the parameters of an interval type-2 fuzzy logic control (IT2FLC) for an inverted pendulum (IP). The statistical results produced by the SOSDA for both benchmark functions and the IP show that the proposed algorithm significantly outperformed the SDA. The SOSDA-based IT2FLC scheme also produced better IP responses than the SDA-based IT2FLC

Opposition based Spiral Dynamic Algorithm with an Application to a PID Control of a Flexible Manipulator

This paper presents an improved version of a Spiral Dynamic Algorithm (SDA). The original SDA is a relatively simple optimization algorithm. It uses a spiral strategy to move search agents within the feasible search space. However, SDA suffers from a premature convergence due to an unbalanced diversification and intensification throughout its search operation. Hence, the algorithm unable to acquire an optimal accuracy solution. An Opposition learning is adopted into SDA to improve the searching strategy of the SDA agents. Therefore in the proposed strategy, a random and a deterministic approaches are synergized and complement each other. The algorithm is tested on several benchmark functions in comparison to the original SDA. A statistical nonparametric Wilcoxon sign rank test is conducted to analyze the accuracy achievement of both algorithms. For solving a real world application, the algorithms are applied to optimize a PID controller for a flexible manipulator system. Result of the test on the benchmark functions shows that the Opposition based SDA outperformed the SDA significantly. For solving the PID control design, both algorithms acquire PID parameters and hence can control the flexible manipulator very well. However, the proposed algorithm shows a better control response

Manta ray foraging optimization with quasi-reflected opposition strategy for global optimization

This paper proposes an extension of Manta Ray Foraging Optimization (MRFO) using Oppositional-based Learning (OBL) technique called Quasi Reflected Opposition (QRO). MRFO is a new algorithm that developed based on the nature of a species in cartilaginous fish called Manta Ray. Manta ray employs three foraging strategies which are chain, cyclone and somersault foraging. Nonetheless, MRFO is tends to getting trap into local optima due to the redundant of intensification of the search agents in the search space. On the other side, OBL is a prominent technique in reducing chance of local optimum while increasing the convergence speed. Thus, QRO is synergized into MRFO to form QR-MRFO, in objective to improve MRFO in term of finding better accuracy of solution and faster convergence rate. Latter, QR-MRFO was performed on a series of benchmark functions and analyzed using statistical non-parametric test of Wilcoxon to measure the significant level of improvement. Results from the test shows that MRFO is undoubtedly defeated by QR-MRFO in term of accuracy

A time series analysis of the relationship between total area planted, palm oil price and production of Malaysian palm oil

Malaysia is a very well-known country in the oils and fats sector especially palm oil because it is the world’s biggest producer and exporter of the commodity. This study was conducted to analyze the relationship between total area planted and palm oil price with production of palm oil in terms of magnitude and direction using the time series analysis method. Johansen cointegration technique, error correction model and Granger causality tests were used to estimate those relationships. The findings showed that the total area planted and palm oil price have negative relationship towards production of Malaysian palm oil. On the other hand, there is no causality relationship between total area planted and production of Malaysian palm oil in the short run. However, there is a unidirectional causality relationship between palm oil price and production of palm oil in Malaysia. For future recommendation, it is suggested that other researchers will supplement this research by integrating other factors that might affect the production of palm oil such as climate change and geographical area

Adaptive levy flight distribution algorithm for solving a dynamic model of an electric heater

This paper presents an improved version of Levy Flight Distribution (LFD) algorithm. The original LFD is formulated based on the random walk strategy. However, it suffers a premature convergence due to imbalance exploration and exploitation. Consequently, the algorithm produces unsatisfactory performance in terms of its final accuracy achievement. As a solution to the problem, an adaptive scheme of search agents step size is incorporated into the original LFD algorithm. Moreover, a mating strategy is also adopted to improve its stochastic nature throughout the search process. The algorithm is applied to optimize a nonlinear dynamic model of an electric water heater. A fuzzy-based Hammerstein structure is adopted to represent the heater model. It comprises a combination of both linear and nonlinear equations so that it can capture the dynamic behavior of the heater satisfactorily. The proposed adaptive LFD algorithm is compared with the original LFD algorithm. The result shows that the proposed algorithm has attained a better accuracy. It also has captured the dynamic behavior of the heater more adequately

Comparative study on thermal performance of cross-matrix absorber solar collector with series and parallel configurations

This paper presents an experimental study comprising two CMA solar collectors with parallel and series arrangements on a forced convection solar drying system. The parallel and series solar collectors were investigated to evaluate the arrangement type’s effect on the thermal performance. The experiments were conducted using artificial solar radiation that varies from 300 to 900W/m2 with the air velocity of 0.5–2 m/s. The arrangement’s efficiency was evaluated based on the drying chamber’s thermal delivery from the collectors, thermal gains, and drying efficiencies, including air velocity effect and pressure drop. Results show that the solar collectors’ parallel arrangement leads to higher air temperature inside the drying chamber than the series by 3.87 ◦C. The thermal efficiency of 33.89% is achieved for the parallel setup than the series of 27.73%. The series arrangement is superior to the parallel in terms of the pressure drop across the solar drying system. Drying efficiency is observed at a higher air velocity of 2 m/s for both arrangements than lower airflow of 0.5 and 1 m/s. Parallel configuration showed promising performance in drying efficiency and low energy usage compared to the series arrangement in which the negative impact of higher pressure-drop was compensated

Simulated kalman filter algorithm with improved accuracy

This paper presents an improved Simulated Kalman Filter optimiza-tion algorithm. It is a further enhancement of a Simulated Kalman Filter (SKF) optimization algorithm. SKF is a random based optimization algorithm inspired from Kalman Filter theory. An exponential term is introduced into Estimation stage of SKF to speed up the searching process and gain more chances in find-ing better solutions. Cost function value that represent an accuracy of a solution is considered as the ultimate goal. Every single agent carries an information about the accuracy of a solution in which will be used to compare with other so-lutions from other agents. A solution that has a lower cost function is consid-ered as the best solution. The algorithm is tested with various benchmark func-tions and compared with the original SKF algorithm. Result of the analysis on the accuracy tested on the benchmark functions shows that the proposed algo-rithm outperforms SKF significantly

Adaptive-somersault MRFO for global optimization with an application to optimize PD control

This paper presents an Adaptive-Somersault Manta Ray Foraging Algorithm (AS-MRFO). Manta Ray Foraging Algorithm (MRFO) is a recently introduced algorithm inspired from Manta Ray Foraging strategy. MRFO is proven as a good performance optimization algorithm in finding a theoretical optima solution of various optimization benchmark functions. It has a considerable high accuracy performance as compared with other state-of-the-art algorithms. In this work, an adaptive position update sine-based formula is adopted into the original MRFO as a strategy to improve its exploration and exploitation strategies. The proposed algorithm is tested on Evolutionary benchmark functions (CEC) to show its accuracy performance. It is also applied to optimize Proportional-Derivative (PD) control for a flexible manipulator system. Result of the performance test shows that the proposed adaptive algorithm has significantly outperformed the accuracy of the original MRFO. The application of the algorithm to optimize the PD control shows that the control scheme optimized by the proposed adaptive-somersault algorithm has attained a better control performance