Nature-inspired Metaheuristic Optimization Algorithm Research Articles

Improved Cuckoo Search algorithmic variants for constrained nonlinear optimization

Although Cuckoo Search (CS) is a quite new nature-inspired metaheuristic optimization algorithm, it has been extensively used in engineering applications, since it has been proven very efficient in solving complex nonlinear problems. In this paper, efficient modifications have been made to the original CS algorithm to enhance its efficiency and robustness. More specifically, constant parameters of the algorithm, such as the probability of the alien egg being discovered by the host bird and the step size of Levy flights have been dynamically tuned. In addition, static and dynamic penalty functions are introduced within the optimization formulation. Finally, a hybrid optimization approach is developed to combine the advantages of CS with those of Bird Swarm Algorithm (BSA). Benchmark problems, widely used in relevant studies, have been solved and the obtained solutions are compared with those previously reported using the standard CS algorithm and other popular evolutionary optimization techniques (i.e., Genetic Algorithms, Particle Swarm Optimization, etc.).

Multi-point shortest path planning based on an Improved Discrete Bat Algorithm

Multi-point shortest path planning problem is a typical problems in discrete optimization. The bat algorithm is a nature-inspired metaheuristic optimization algorithm that is used in a wide range of fields. However, there is one problem with the BA, which is easy to premature. To solve multi-point shortest path planning problem, an improved discrete bat algorithm (IDBA) is proposed in this paper. In this algorithm, the Floyd–Warshall algorithm is first used to transform an incomplete connected graph into a complete graph whose vertex set consists of a start point and necessary points. Then the algorithm simulates the bats’ foraging and obstacle avoidance process to find the shortest path in the complete graph to satisfy the constraints. Finally, the path is transferred to the original incomplete graph to get the solution. In order to overcome the premature phenomenon of a discrete bat algorithm, the modified neighborhood operator is proposed. To prove the effectiveness of our method, we compared its performance in 26 instances with the results obtained by three different algorithms: DBA, IBA and GSA-ACS-PSOT. We also performed a sensitivity analysis on the parameters. The results indicate that the improved bat algorithm outperforms all the other alternatives in most cases.

Enhanced Nature-Inspired Meta-Heuristic Algorithm for Microgrid Performance Improvement

In this article, a new selection technique based on Enhanced Nature-Inspired Meta-Heuristic (ENIMH) optimization algorithm is presented to improve the Microgrid (MG) dynamic performance. Interconnected microgrids have the ability to provide a clean and sustainable energy during normal and emergency operating conditions. The concerned microgrid includes hybrid renewable energy sources (RES) and energy storages systems (ESS). MG achieves a reduced dependency on the electric grid and provides flexible and adaptive energy supply. This paper develops a new selection technique based on ENIMH optimization that distinguishes the degrees of resemblance between the best individual and other individuals of current population. This technique proposes a binary coding of individuals, and is compared to conventional techniques; it allows each individual to occupy a section of the modified roulette wheel selection for the calculated degree of resemblance. This enhanced optimization technique tunes the dynamic PID parameters of microgrid closed loop system. The designed strategy is dependably to locate the arrangement of enhanced parameters to minimize the system frequency fluctuations in the microgrid and to provide the improved dynamic performance by being sensitive to variations for closed loop response under various power and load conditions. The proposed technique has been demonstrated using Matlab/Simulink simulation on the underlined microgrid, where the achieved results confirm the effectiveness of proposed selection method for the reproduction of best individuals to show the improved performance. The proposed technique achieved satisfactory performance for PID-controllers, and provided a good closed loop performance, minimum overshoot and minimum fitness index, in comparison with other well-established methods. The results emphasize that ENIMH optimization algorithm has the exploration and exploitation capability of population best individuals to accomplish the best solutions.

Performance comparison of five metaheuristic nature-inspired algorithms to find near-OGRs for WDM systems

The metaheuristic approaches inspired by the nature are becoming powerful optimizing algorithms for solving NP-complete problems. This paper presents five nature-inspired metaheuristic optimization algorithms to find near-optimal Golomb ruler (OGR) sequences in a reasonable time. In order to improve the search space and further improve the convergence speed and optimization precision of the metaheuristic algorithms, the improved algorithms based on mutation strategy and Levy-flight search distribution are proposed. These two strategies help the metaheuristic algorithms to jump out of the local optimum, improve the global search ability so as to maintain the good population diversity. The OGRs found their potential application in channel-allocation method to suppress the four-wave mixing crosstalk in optical wavelength division multiplexing systems. The results conclude that the proposed algorithms are superior to the existing conventional computing algorithms i.e. extended quadratic congruence and search algorithm and nature-inspired optimization algorithms i.e. genetic algorithms, biogeography based optimization and simple big bang–big crunch to find near-OGRs in terms of ruler length, total optical channel bandwidth and computation time. The idea of computational complexity for the proposed algorithms is represented through the Big O notation. In order to validate the proposed algorithms, the non-parametric statistical Wilcoxon analysis is being considered.

Optimal Portfolio Management for Engineering Problems Using Nonconvex Cardinality Constraint: A Computing Perspective

The problem of portfolio management relates to the selection of optimal stocks, which results in a maximum return to the investor while minimizing the loss. Traditional approaches usually model the portfolio selection as a convex optimization problem and require the calculation of gradient. Note that gradient-based methods can stuck at local optimum for complex problems and the simplification of portfolio optimization to convex, and further solved using gradient-based methods, is at a high cost of solution accuracy. In this paper, we formulate a nonconvex model for the portfolio selection problem, which considers the transaction cost and cardinality constraint, thus better reflecting the decisive factor affecting the selection of portfolio in the real-world. Additionally, constraints are put into the objective function as penalty terms to enforce the restriction. Note that this reformulated problem cannot be readily solved by traditional methods based on gradient search due to its nonconvexity. Then, we apply the Beetle Antennae Search (BAS), a nature-inspired metaheuristic optimization algorithm capable of efficient global optimization, to solve the problem. We used a large real-world dataset containing historical stock prices to demonstrate the efficiency of the proposed algorithm in practical scenarios. Extensive experimental results are presented to further demonstrate the efficacy and scalability of the BAS algorithm. The comparative results are also performed using Particle Swarm Optimizer (PSO), Genetic Algorithm (GA), Pattern Search (PS), and gradient-based fmincon (interior-point search) as benchmarks. The comparison results show that the BAS algorithm is six times faster in the worst case (25 times in the best case) as compared to the rival algorithms while achieving the same level of performance.

A Comparative Study of Nature-Inspired Metaheuristic Algorithms in Search of Near-to-optimal Golomb Rulers for the FWM Crosstalk Elimination in WDM Systems

ABSTRACTNowadays, nature-inspired metaheuristic algorithms are the most powerful optimizing algorithms for solving NP-complete problems. This paper proposes five recent approaches to find near-optimal Golomb ruler (OGR) sequences based on nature-inspired algorithms in a reasonable time. The optimal Golomb ruler sequences found their application in channel-allocation method that allows suppression of the crosstalk due to four-wave mixing (FWM) in optical wavelength division multiplexing (WDM) systems. The simulation results conclude that the proposed nature-inspired metaheuristic optimization algorithms are superior to the existing conventional computing algorithms, i.e., Extended Quadratic Congruence (EQC) and Search algorithm (SA) and nature-inspired algorithms, i.e., Genetic algorithms (GAs), Biogeography-based optimization (BBO) and simple Big bang–Big crunch (BB-BC) optimization algorithm to find near-OGRs in terms of ruler length, total optical channel bandwidth and computation time.

A cryptanalytic attack of simplified-AES using ant colony optimization

Ant colony Optimization is a nature-inspired meta-heuristic optimization algorithm that gained a great interest in resolution of combinatorial and numerical optimization problems in many science and engineering domains. The aim of this work was to investigate the use of Ant Colony Optimization in cryptanalysis of Simplified Advanced Encryption Standard (S-AES), using a known plaintext attack. We have defined the essential components of our algorithm such as heuristic value, fitness function and the strategy to update pheromone trails. It is shown from the experimental results that our proposed algorithm allow us to break S-AES cryptosystem after exploring a minimum search space when compared with others techniques and requiring only two plaintext-ciphertext pairs.

Cuckoo Search Algorithm for Test Case Prioritization in Regression Testing

There are countless optimization problems that have been accelerated by Nature Inspired Metaheuristic Optimization Algorithms (NIMOA) in the earlier decades. NIMOA have gained huge popularity owing to their effective results. In this study NIMOA namely, Cuckoo Search Algorithm (CSA) is used to prioritize (order) the test cases for Regression Testing (RT). Prioritizations aids in the execution of higher priority test cases to give early fault detection. This research adopts the aggressive approach of reproduction made by Cuckoos to prioritize the test cases for RT. Average Percentage of Fault Detected (APFD) metrics is used in this paper for validations of results. APFD metrics is used to compare the performance of CSA with Flower Pollination Algorithm (FPA) and traditional approaches for Test Case Prioritization (TCP). Two java applications are used for the study. CSA is implemented in Java on eclipse platform. It is learnt from the study that APFD results of CSA outperformed the FPA for both the applications namely Puzzle Game and AreaandPerimeter. It is inferred from the results that prioritized set of test cases given by NIMOA outperformed the APFD results of traditional approaches and also CSA performed better than the FPA for TCP.

Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo .

Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm

The principle of any approach for solving feature selection problem is to find a subset of the original features. Since finding a minimal subset of the features is an NP-hard problem, it is necessary to develop and propose practical and efficient heuristic algorithms. The whale optimization algorithm is a recently developed nature-inspired meta-heuristic optimization algorithm that imitates the hunting behavior of humpback whales to solve continuous optimization problems. In this paper, we propose a novel binary whale optimization algorithm (BWOA) to solve feature selection problem. BWOA is especially desirable and appealing for feature selection problem whenever there is no heuristic information that can lead the search to the optimal minimal subset. Nonetheless, whales can find the best features as they hunt the prey. Rough set theory (RST) is one of the effective algorithms for feature selection. We use RST with BWOA as the first experiment, and in the second experiment, we use a wrapper approach with BWOA on three different classifiers for feature selection. Also, we verify the performance and the effectiveness of the proposed algorithm by performing our experiments using 32 datasets from the UCI machine learning repository and comparing the proposed algorithm with some powerful existing algorithms in the literature. Furthermore, we employ two nonparametric statistical tests, Wilcoxon Signed-Rank test, and Friedman test, at 5% significance level. Our results show that the proposed algorithm can provide an efficient tool to find a minimal subset of the features.

Binary tree-seed algorithms with S-shaped and V-shaped transfer functions

Tree-seed algorithm (TSA) is a nature-inspired metaheuristic optimization algorithm. TSA is proposed for solving continuous optimization problems. In this work, TSA is modified with transfer functions for solving binary optimization problems. Continuous search space is mapped to binary search space with transfer functions. Four S-shaped and four V-shaped transfer functions are used for discretization. Uncapacitated facility location problem (UFLP) is a pure binary optimization problem. In order to measure the performance, 15 different sized (small, medium, large and extra-large) UFLPs are solved with eight different binary TSA in this work. Experimental results show that S-shaped transfer functions are better than V-shaped transfer functions on these problem sets.

Optimal sizing of an autonomous photovoltaic/wind/battery/diesel generator microgrid using grasshopper optimization algorithm

This article focuses on the application of a latest nature-inspired metaheuristic optimization algorithm named Grasshopper Optimization Algorithm (GOA) in the area of microgrid system sizing design problem. The proposed algorithm is applied to an autonomous microgrid system in order to determine the optimal system configuration that will supply energy demand reliably based on the deficiency of power supply probability (DPSP) and cost of energy (COE). Firstly, a robust rule-based energy management scheme (EMS) is proposed to coordinate the power flow among the various system components that formed the microgrid. Then, the GOA is integrated with the EMS to perform the optimal sizing for the hybrid autonomous microgrid for five units of residential in an off-grid location in Yobe State, Nigeria. The proposed microgrid comprises of photovoltaic modules, wind turbine, battery storage system and a diesel generator. The effectiveness of the proposed GOA in solving the optimization problem is examined and its performance is compared with particle swarm optimization (PSO) and cuckoo search (CS) optimization algorithm. In addition, a sensitivity analysis is performed on the COE to highlight the impact of varying sensitive system inputs. The proposed optimization is programmed using MATLAB simulation package. The simulation results confirm that GOA is able to optimally size the system as compared to its counterparts, CS and PSO. In which, a decrement of 14% and 19.3% is achieved in the system capital cost, respectively.

A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm.

The whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC and PSO. Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta-analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey's results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.

The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems

Nature-inspired optimization algorithms, especially swarm based algorithms (SAs), solve many scientific and engineering problems due to their flexibility and simplicity. These algorithms are applicable for optimization problems without structural modifications. This work presents a novel nature-inspired metaheuristic optimization algorithm, called SailFish Optimizer (SFO), which is inspired by a group of hunting sailfish. This method consists of two tips of populations, sailfish population for intensification of the search around the best so far and sardines population for diversification of the search space. The SFO algorithm is evaluated on 20 well-known unimodal and multimodal mathematical functions to test different characteristics of the algorithm. In addition, SFO is compared with the six state-of-art metaheuristic algorithms in low and high dimensions. It also indicates competitive results for improvement of exploration and exploitation phases, avoidance of local optima, and high speed convergence especially on large-scale global optimization. The SFO algorithm outperforms the best algorithms in the literature on the majority of the test functions and it shows the statistically significant difference among other algorithms. Moreover, the SFO algorithm shows significantly great results for non-convex, non-separable and scalable test functions. Eventually, the promising results on five real world optimization problems indicate that the SFO is applicable for problem solving with constrained and unknown search spaces.

Optimal swarm decomposition with whale optimization algorithm for weak feature extraction from multicomponent modulation signal

Abstract Since multicomponent modulation and complicated interference simultaneously exist in the vibration signals caused by the bearing compound fault, the fault feature becomes rather weak and is hard to be extracted. Therefore, the diagnosis of bearing compound fault is always considered as the bottle neck issue of machinery condition monitoring. The decomposition methods which can separate the individual component into different modes with appropriate criterion provide an alternative for solving this issue. However, various weaknesses, including mode mixing, depending on predefined mode number and poor noise suppression, seriously restrict their application range. To overcome the disadvantages, a novel decomposition method, swarm decomposition (SWD), is initially introduced in the diagnosis of the machinery fault. Inspired by the swarm intelligence, SWD can intelligently decompose the signal by using the swarm filtering and iterative algorithm. A series of analysis, including an impulse responses test and a numerical validation, verifies SWD has a remarkable performance in the decomposition of multicomponent modulation signal. Yet, the superiority also relies on the choice of thresholds. Therefore, a novel nature-inspired meta-heuristic optimization algorithm, whale optimization algorithm (WOA), is applied to solve the problem. Benefiting from the virtues of SWD and WOA, the proposed optimal swarm decomposition (OSWD) is more suitable for the weak feature extraction from multicomponent modulation signal. Finally, to further highlight its superiority in the diagnosis of the bearing compound fault, a number of simulation and real datasets from the axle box bearings of locomotive are applied. Results show that OSWD can be considered as an alternative to address the bottle neck issue from the bearing compound fault.

Automatic Data Clustering Using Hybrid Firefly Particle Swarm Optimization Algorithm

The firefly algorithm is a nature-inspired metaheuristic optimization algorithm that has become an important tool for solving most of the toughest optimization problems in almost all areas of global optimization and engineering practices. However, as with other metaheuristic algorithms, the performance of the firefly algorithm depends on adequate parameter tuning. In addition, its diversification as a global metaheuristic can lead to reduced speed, as well as an associated decrease in the rate of convergence when applied to solve problems with large number of variables such as data clustering problems. Clustering is an unsupervised data analysis technique used for identifying homogeneous groups of objects based on the values of their attributes. To mitigate the aforementioned drawbacks, an improved firefly algorithm is hybridized with the well-known particle swarm optimization algorithm to solve automatic data clustering problems. To investigate the performance of the proposed hybrid algorithm, it is compared with four popular metaheuristic methods from literature using twelve standard datasets from the UCI Machine Learning Repository and the two moons dataset. The extensive computational experiments and results analysis carried out shows that the proposed algorithm not only achieves superior performance over the standard firefly and particle swarm optimization algorithms, but also exhibits high level of stability and can be efficiently utilized to solve other clustering problems with high dimensionality.

Satellite Image De-Noising With Harris Hawks Meta Heuristic Optimization Algorithm and Improved Adaptive Generalized Gaussian Distribution Threshold Function

An image may be influenced by noise during capturing and transmitting process. Removing the possible noise from the image has always been a challenging issue due to this fact that further processing will not be possible unless by diminishing the noise from images. Many researchers attempted to remove the noise to improve the qualitative and also the quantitative results but these methods could not preserve the quality of images after applying de-noising techniques. In this paper, in the first stage, we utilized the most recent nature-inspired meta-heuristic optimization algorithm to get the optimal solutions for the parameters of thresholding function. Using the Harris hawk optimization (HHO) algorithm results in obtaining the optimized thresholded wavelet coefficients before applying the inverse wavelet transform. In the second stage, we proposed the improved adaptive generalized Gaussian distribution (AGGD) threshold, which is a data-driven function with an adaptive threshold value. This function can be fitted to any kind of images without using any shape tuning parameter. It is clear that the calculation of the threshold value does not require any optimization and LMS learning algorithm. The qualitative and quantitative results validate the superiority of the proposed method.

Rain-Fall Optimization Algorithm with new parallel implementations

Rainfall Optimization Algorithm (RFO) is a nature-inspired metaheuristic optimization algorithm. RFO mimics the movement of water drops generated during rainfall to optimize a function. The paper study new implementations for RFO to offer more reliable results. Moreover, it studies three restarting techniques that can be applied to the algorithm with multithreading. The different implementations for the RFO are benchmarked to test and verify the performance and accuracy of the solutions. The paper presents and compares the results using several multidimensional testing functions, as well as the visual behavior of the raindrops inside the benchmark functions. The results confirm that the movement of the artificial drops corresponds to the natural behavior of raindrops. The results also show the effectiveness of this behavior to minimize an optimization function and the advantages of parallel computing restarting techniques to improve the quality of the solutions.

A Novel Whale Optimization Algorithm for Cryptanalysis in Merkle-Hellman Cryptosystem

With the advance of the communication technology and the massive flow of information across the internet, it is becoming urgent to keep the confidentiality of the transmitted information. Using the internet has been extended to several fields such as e-mail, e-commerce, e-learning, health and medicine, shopping, and so on. Cryptography is the study of different techniques for securing the communication between the sender and the receiver. One of the most known cryptosystems is Merkle–Hellman Knapsack Cryptosystem (MHKC). It is one of the earliest Public Key Cryptosystem (PKC) that is used to secure the messages between the sender and the receiver. Developing a powerful cryptosystem comes after studying the fragility points of the current cryptosystems. The Whale Optimization Algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social behavior of humpback whales. WOA has validated excellent performance in solving the continuous problems and the engineering optimization problems. This paper introduces a novel Modified version of WOA (MWOA) for cryptanalysis of MHKC. The sigmoid function is used to map the continuous values into discrete one. A penalty function is added to the evaluation function to deal with the infeasible solutions. The mutation operation is employed for improving the solutions. The results show that MWOA is more effective and robust than other algorithms in the literature.

A modified nature inspired meta-heuristic whale optimization algorithm for solving 0–1 knapsack problem

The Whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social character of the humpback whales. WOA has an efficient performance in solving the continuous problems and engineering optimization problems. This paper presents an improved whale optimization algorithm (IWOA) for solving both single and multidimensional 0–1 knapsack problems with different scales. A penalty function is added to the evaluation function so that the fitness of the feasible solutions can outperform the fitness of the infeasible ones. The sigmoid function can take the real-valued solutions as input and produces the binary solutions as output. A two-stage repair operator is employed for handling the infeasible solutions. IWOA can give a better tradeoff between the diversification and the intensification through using two strategies: Local Search Strategy (LSS) and the Levy flight walks. In addition to the bitwise operation which increases the IWOA efficiency. The proposed IWOA is compared with other state-of-art algorithms to validate the effectiveness of the proposed algorithm in solving 0–1 knapsack problems. Further, the experimental results and extensive numerical illustrations have been indicated that IWOA is efficient, effective, and robust for solving the hard 0–1 knapsack problems than the other existing approaches in the available literature. The source code of the IWOA algorithm will be available after the paper accepted as a toolbox in MATLAB library.