Showing papers on "Metaheuristic published in 2021"
TL;DR: Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms.
1,218 citations
TL;DR: The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems.
Abstract: The difficulty and complexity of the real-world numerical optimization problems has grown manifold, which demands efficient optimization methods. To date, various metaheuristic approaches have been introduced, but only a few have earned recognition in research community. In this paper, a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) is introduced to solve the optimization problems. AOA is devised with inspirations from an interesting law of physics Archimedes’ Principle. It imitates the principle of buoyant force exerted upward on an object, partially or fully immersed in fluid, is proportional to weight of the displaced fluid. To evaluate performance, the proposed AOA algorithm is tested on CEC’17 test suite and four engineering design problems. The solutions obtained with AOA have outperformed well-known state-of-the-art and recently introduced metaheuristic algorithms such genetic algorithms (GA), particle swarm optimization (PSO), differential evolution variants L-SHADE and LSHADE-EpSin, whale optimization algorithm (WOA), sine-cosine algorithm (SCA), Harris’ hawk optimization (HHO), and equilibrium optimizer (EO). The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/79822-archimedes-optimization-algorithm
444 citations
TL;DR: The proposed African Vultures Optimization Algorithm (AVOA) is named and simulates African vultures’ foraging and navigation behaviors and indicates the significant superiority of the AVOA algorithm at a 95% confidence interval.
431 citations
TL;DR: The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments, and the results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.
Abstract: In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-of-the-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.
398 citations
TL;DR: Wang et al. as mentioned in this paper proposed a new metaheuristic algorithm inspired by the collective intelligence of natural organisms in nature, called Artificial Gorilla Troops Optimizer (GTO).
Abstract: Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems. Friedman's test and Wilcoxon rank-sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high-dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics.
316 citations
TL;DR: The Colony Predation Algorithm (CPA) as mentioned in this paper is based on the corporate predation of animals in nature and utilizes a mathematical mapping following the strategies used by animal hunting groups, such as dispersing prey, encircling prey, supporting the most likely successful hunter, and seeking another target.
263 citations
TL;DR: The JS algorithm was used to solve structural optimization problems, including 25- bar tower design and 582-bar tower design problems, where JS not only performed best but also required the fewest evaluations of objective functions.
232 citations
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics to predict the number of the COVID-19 cases.
167 citations
TL;DR: A nature-inspired swarm-based metaheuristic for solving global optimization problems called Golden Eagle Optimizer (GEO), which shows GEO’s superiority, which indicates that it can find the global optimum and avoid local optima effectively.
162 citations
TL;DR: This algorithm imitates the huddling and swarm behaviors of emperor penguin optimizer and salp swarm algorithm, respectively, which reveals that ESA offers optimal solutions as compared to the other competitor algorithms.
Abstract: In this paper, a hybrid bio-inspired metaheuristic optimization approach namely emperor penguin and salp swarm algorithm (ESA) is proposed. This algorithm imitates the huddling and swarm behaviors of emperor penguin optimizer and salp swarm algorithm, respectively. The efficiency of the proposed ESA is evaluated using scalability analysis, convergence analysis, sensitivity analysis, and ANOVA test analysis on 53 benchmark test functions including classical and IEEE CEC-2017. The effectiveness of ESA is compared with well-known metaheuristics in terms of the optimal solution. The proposed ESA is also applied on six constrained and one unconstrained engineering problems to evaluate its robustness. The results reveal that ESA offers optimal solutions as compared to the other competitor algorithms.
149 citations
TL;DR: In this paper, a novel metaheuristic algorithm called Chaos Game Optimization (CGO) is developed for solving optimization problems and the obtained results proved that the CGO is superior compared to the other metaheuristics in most of the cases.
Abstract: In this paper, a novel metaheuristic algorithm called Chaos Game Optimization (CGO) is developed for solving optimization problems The main concept of the CGO algorithm is based on some principles of chaos theory in which the configuration of fractals by chaos game concept and the fractals self-similarity issues are in perspective A total number of 239 mathematical functions which are categorized into four different groups are collected to evaluate the overall performance of the presented novel algorithm In order to evaluate the results of the CGO algorithm, three comparative analysis with different characteristics are conducted In the first step, six different metaheuristic algorithms are selected from the literature while the minimum, mean and standard deviation values alongside the number of function evaluations for the CGO and these algorithms are calculated and compared A complete statistical analysis is also conducted in order to provide a valid judgment about the performance of the CGO algorithm In the second one, the results of the CGO algorithm are compared to some of the recently developed fractal- and chaos-based algorithms Finally, the performance of the CGO algorithm is compared to some state-of-the-art algorithms in dealing with the state-of-the-art mathematical functions and one of the recent competitions on single objective real-parameter numerical optimization named “CEC 2017” is considered as numerical examples for this purpose In addition, a computational cost analysis is also conducted for the presented algorithm The obtained results proved that the CGO is superior compared to the other metaheuristics in most of the cases
TL;DR: In this paper, a hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSPO) was proposed to minimize the total energy consumed by mobile devices and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of mobile devices, allocated bandwidth of available channels, and transmission power of each mobile device in each time slot.
Abstract: Smart mobile devices (SMDs) can meet users’ high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth and inevitably increases the latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve a balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels, and transmission power of each SMD in each time slot. It jointly considers the execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and the maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life data-based experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.
TL;DR: A self-adaptive differential evolution algorithm is developed for addressing a single BPM scheduling problem with unequal release times and job sizes and results demonstrate that the proposed self- Adaptive algorithm is more effective than other algorithms for this scheduling problem.
Abstract: Batch-processing machines (BPMs) can process a number of jobs at a time, which can be found in many industrial systems. This article considers a single BPM scheduling problem with unequal release times and job sizes. The goal is to assign jobs into batches without breaking the machine capacity constraint and then sort the batches to minimize the makespan. A self-adaptive differential evolution algorithm is developed for addressing the problem. In our proposed algorithm, mutation operators are adaptively chosen based on their historical performances. Also, control parameter values are adaptively determined based on their historical performances. Our proposed algorithm is compared to CPLEX, existing metaheuristics for this problem and conventional differential evolution algorithms through comprehensive experiments. The experimental results demonstrate that our proposed self-adaptive algorithm is more effective than other algorithms for this scheduling problem.
TL;DR: This review study serves as a solid reference for future studies in the arena of SI and in particular the MBO algorithm including its modifications, hybridizations, variants, and applications.
Abstract: Swarm intelligence (SI) is the collective behavior of decentralized, self-organized natural or artificial systems. Monarch butterfly optimization (MBO) algorithm is a class of swarm intelligence metaheuristic algorithm inspired by the migration behavior of monarch butterflies. Through the migration operation and butterfly adjusting operation, individuals in MBO are updated. MBO can outperform many state-of-the-art optimization techniques when solving global numerical optimization and engineering problems. This paper presents a comprehensive review of the MBO algorithm including its modifications, hybridizations, variants, and applications. Additionally, further research directions for MBO are discussed. This review study serves as a solid reference for future studies in the arena of SI and in particular the MBO algorithm.
TL;DR: This in-depth research introduced horizontal crossover search and vertical crossover search into the ACOR and improved the selection mechanism of the original ACOR to form an improved algorithm (CCACO) for the first time.
Abstract: The ant colony optimization (ACO) is the most exceptionally fundamental swarm-based solver for realizing discrete problems. In order to make it also suitable for solving continuous problems, a variant of ACO (ACOR) has been proposed already. The deep-rooted ACO always stands out in the eyes of well-educated researchers as one of the best-designed metaheuristic ways for realizing the solutions to real-world problems. However, ACOR has some stochastic components that need to be further improved in terms of solution quality and convergence speed. Therefore, to effectively improve these aspects, this in-depth research introduced horizontal crossover search (HCS) and vertical crossover search (VCS) into the ACOR and improved the selection mechanism of the original ACOR to form an improved algorithm (CCACO) for the first time. In CCACO, the HCS is mainly intended to increase the convergence rate. Meanwhile, the VCS and the developed selection mechanism are mainly aimed at effectively improving the ability to avoid dwindling into local optimal (LO) and the convergence accuracy. To reach next-level strong results for image segmentation and better illustrate its effectiveness, we conducted a series of comparative experiments with 30 benchmark functions from IEEE CEC 2014. In the experiment, we compared the developed CCACO with well-known conventional algorithms and advanced ones. All experimental results also show that its convergence speed and solution quality are superior to other algorithms, and its ability to avoid dropping into local optimum (LO) is more reliable than that of its peers. Furthermore, to further illustrate its enhanced performance, we applied it to image segmentation based on multi-threshold image segmentation (MTIS) method with a non-local means 2D histogram and Kapur's entropy. In the experiment, it was compared with existing competitive algorithms at low and high threshold levels. The experimental results show that the proposed CCACO achieves excellent segmentation results at both low and high threshold levels. For any help and guidance regarding this research, readers, and industry activists can refer to the background info at http://aliasgharheidari.com/ .
TL;DR: In this paper, a double adaptive weight mechanism was introduced into the MFO algorithm, termed as WEMFO, to boost the search capability of the basic MFO and provide a more efficient tool for optimization purposes.
Abstract: Moth flame optimization (MFO) is a swarm-based algorithm with mediocre performance and marginal originality proposed in recent years. It tried to simulate the fantasy navigation mode of moth lateral positioning. The basic MFO has no specific, deep strategies in different periods of the algorithm and a fragile evolutionary basis, which may lead to the problem of falling into local optimum and slow convergence trend. Therefore, this paper introduces a double adaptive weight mechanism into the MFO algorithm, termed as WEMFO, to boost the search capability of the basic MFO and provide a more efficient tool for optimization purposes. The proposed WEMFO adjusts the search strategy adaptively in different periods of the algorithm, making it more flexible between global search (diversification) and local search (intensification). The WEMFO algorithm is compared with some illustrious metaheuristic solvers and advanced metaheuristic methods developed in recent years on thirty benchmark functions. The experimental results expose that the developed WEMFO has apparent compensations in terms of convergence speed and solution accuracy. Moreover, this paper analyzes the diversity and balance of WEMFO and applies the algorithm to several engineering problems. The experimental results show that the WEMFO algorithm has good performance in engineering problems. Additionally, the proposed WEMFO was also applied to train Kernel Extreme Learning Machine (KELM), the resultant optimized WEMFO-KELM model was applied to six clinical disease classification problems. By comparing with MFO-KELM and other five classification models, the experimental results showed that the proposed algorithm had shown better performance in practical problems. An online guide for the algorithm in this research WEMFO and proposed classifier WEMFO-KELM will be publicly available at https://aliasgharheidari.com .
TL;DR: The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm with Simulated Annealing with WOA, and is compared with several state‐of‐the‐art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA.
Abstract: © 2020 John Wiley & Sons, Ltd. Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state-of-the-art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches.
TL;DR: This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency.
Abstract: In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.
TL;DR: An overview of the Ant Lion Optimizer (ALO) applications and a review of ALO variants is presented, which include binary, modification, hybridization, enhanced, and others.
Abstract: This paper introduces a comprehensive overview of the Ant Lion Optimizer (ALO). ALO is a novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the hunting behavior of ant lions in nature life. The review is highlighted the applications that are utilized ALO algorithm to solve various optimization problems. In ALO, the best solution is determined to enhance the performance of the functional and efficient during the optimization process by finding the minimum or maximum values to solve a certain problem. Metaheuristic algorithms have become the focus of research due to introduce of decision-making and asses the benefits in solving various optimization problems. Also, a review of ALO variants is presented in this paper such as binary, modification, hybridization, enhanced, and others. The classifications of the ALO’s applications include the benchmark functions, machine learning applications, networks applications, engineering applications, software engineering, and Image processing. Finally, According to the reviewed papers published in the literature, the ALO algorithm is mostly utilized in solving various optimization problems. Presenting an overview and reviewing the ALO applications are the main aims of this review paper.
TL;DR: A comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others is presented in this article.
Abstract: Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.
TL;DR: It is of utmost importance to use a correct tool for measuring the performance of the diverse set of metaheuristic algorithms to derive an appropriate judgment on the superiority of the algorithms and also to validate the claims raised by researchers for their specific objectives.
Abstract: The simulation-driven metaheuristic algorithms have been successful in solving numerous problems compared to their deterministic counterparts. Despite this advantage, the stochastic nature of such algorithms resulted in a spectrum of solutions by a certain number of trials that may lead to the uncertainty of quality solutions. Therefore, it is of utmost importance to use a correct tool for measuring the performance of the diverse set of metaheuristic algorithms to derive an appropriate judgment on the superiority of the algorithms and also to validate the claims raised by researchers for their specific objectives. The performance of a randomized metaheuristic algorithm can be divided into efficiency and effectiveness measures. The efficiency relates to the algorithm’s speed of finding accurate solutions, convergence, and computation. On the other hand, effectiveness relates to the algorithm’s capability of finding quality solutions. Both scopes are crucial for continuous and discrete problems either in single- or multi-objectives. Each problem type has different formulation and methods of measurement within the scope of efficiency and effectiveness performance. One of the most decisive verdicts for the effectiveness measure is the statistical analysis that depends on the data distribution and appropriate tool for correct judgments.
TL;DR: A model-free tracking controller for a cooperative mobile-manipulators to perform tasks individually and cooperatively and a nature-inspired metaheuristic algorithm inspired by the food searching nature of the beetles, ZNNBAS is presented.
TL;DR: A new optimization algorithm named Flow Direction Algorithm (FDA), which is a physics-based algorithm that mimics the flow direction to the outlet point with the lowest height in a drainage basin, demonstrates the superior performance of the FDA in solving challenging problems.
TL;DR: The obtained results demonstrate that the proposed AOS algorithm provides very outstanding results in dealing with the mathematical and engineering design problems.
TL;DR: The proposed SMA-AGDE is a promising optimization tool for global and combinatorial optimization problems and engineering design problems and over different function landscapes.
Abstract: The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this paper proposes a hybrid algorithm: SMA combined to Adaptive Guided Differential Evolution Algorithm (AGDE) (SMA-AGDE). The AGDE mutation method is employed to enhance the swarm agents’ local search, increase the population’s diversity, and help avoid premature convergence. The SMA-AGDE’s performance is evaluated on the CEC’17 test suite, three engineering design problems – tension/compression spring, pressure vessel, and rolling element bearing – and two combinatorial optimization problems – bin packing and quadratic assignment. The SMA-AGDE is compared with three categories of optimization methods: (1) The well-studied MAs, i.e., Biogeography-Based Optimizer (BBO), Gravitational Search Algorithm (GSA), and Teaching Learning-Based Optimization (TLBO), (2) Recently developed MAs, i.e., Harris Hawks Optimization (HHO), Manta Ray Foraging optimization (MRFO), and the original SMA, and (3) High-performance MAs, i.e., Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), and AGDE. The overall simulation results reveal that the SMA-AGDE ranked first among the compared algorithms, and so, over different function landscapes. Thus, the proposed SMA-AGDE is a promising optimization tool for global and combinatorial optimization problems and engineering design problems.
TL;DR: The results in different scenarios demonstrate that as compared with several existing evolutionary algorithms, the CSA method can effectively explore the decision space and produce competitive results in terms of various performance evaluation indicators.
TL;DR: In this paper, a new reinforcement learning (RL)-based control approach that uses the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the Neural Networks (NNs) is presented.
TL;DR: This work proposes a new method for producing highly accurate non-parametric models for wind turbines based on artificial neural networks (ANNs) using networks belonging to the radial basis function (RBF) architecture, and introduces a new training algorithm based on the successful non-symmetric fuzzy means (NSFM) approach.
TL;DR: In this article, a distributed flow shop group scheduling problem is considered and a cooperative co-evolutionary algorithm (CCEA) with a novel collaboration model and a reinitialization scheme is proposed.
Abstract: This article addresses a novel scheduling problem, a distributed flowshop group scheduling problem, which has important applications in modern manufacturing systems. The problem considers how to arrange a variety of jobs subject to group constraints at a number of identical manufacturing cellulars, each one with a flowshop structure, with the objective of minimizing makespan. We explore the problem-specific knowledge and present a mixed-integer linear programming model, a counterintuitive paradox, and two suites of accelerations to save computational efforts. Due to the complexity of the problem, we consider a decomposition strategy and propose a cooperative co-evolutionary algorithm (CCEA) with a novel collaboration model and a reinitialization scheme. A comprehensive and thorough computational and statistical campaign is carried out. The results show that the proposed collaboration model and reinitialization scheme are very effective. The proposed CCEA outperforms a number of metaheuristics adapted from closely related scheduling problems in the literature by a significantly considerable margin.
TL;DR: An unprecedented metaheuristic algorithm was created inspired by the physical phenomenon of radial intra-cloud lightning and Lichtenberg figures, successfully exploiting the fractal power and it is different from many in the literature as it is a hybrid algorithm composed of methods of search based on population and trajectory.
Abstract: This paper proposes a novel global optimization algorithm called Lichtenberg Algorithm (LA), inspired by the Lichtenberg figures patterns. Optimization is an essential tool to minimize or maximize functions, obtaining optimal results on costs, mass, energy, gains, among others. Actual problems may be multimodal, nonlinear, and discontinuous and may not be minimized by classical analytical methods that depend on the gradient. In this context there are metaheuristics algorithms inspired by natural phenomena to optimize real problems. There is no algorithm that is the worst or the best, but more efficient for a given type of problem. Thus, an unprecedented metaheuristic algorithm was created inspired by the physical phenomenon of radial intra-cloud lightning and Lichtenberg figures, successfully exploiting the fractal power and it is different from many in the literature as it is a hybrid algorithm composed of methods of search based on population and trajectory. Several test functions, including a design problem in a welded beam, were used to verify the robustness and to validate the Lichtenberg Algorithm. In all cases, the results were satisfactory when compared to those in the literature. LA shown to be a powerful optimization tool for both unconstraint optimizations and real problems with linear and nonlinear constraints.