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Economic dispatch (ED) problem is a popular optimization problem in electrical engineering. Many studies that handled ED problem employed metaheuristics as the optimization technique. Unfortunately, these metaheuristics are old or the existing ones. Meanwhile, ED problem is not the favourite constrained case in studies that introduced a novel metaheuristic. This research introduces a novel metaheuristic called quadratic time optimization (QTO). QTO comprises three sequential searches where both diversification and intensification are blended in every search. But the portion of both orientations changes by following the quadratic time. The appraisal of QTO is performed by employing it to solve both unconstrained and constrained problems. The 23 traditional functions represent the unconstrained problem while economic load dispatch (ELD) problem with valve point effect and spinning reserve problem represents the constrained problem. In both appraisals, there are five novel metaheuristics represent the benchmarks, including: walrus optimization algorithm (WaOA), coati optimization algorithm (COA), total interaction algorithm (TIA), addax optimization algorithm (AOA), and language education optimization (LEO). The result shows the competitiveness of the proposed QTO in handling both unconstrained and constrained problems. QTO becomes the first best in 8 functions out of 23 functions including five high dimension unimodal functions, two high dimension multimodal functions, and one fixed dimension multimodal functions. The result also shows that QTO is competitive in three use cases in ELD problems as the ratio between the range and average total cost among metaheuristics for the first, second, and third cases is 0.72, 0.04, and 0.02 percent consecutively. [ABSTRACT FROM AUTHOR]

Combinational logic circuits (CLCs) are basic building blocks of a system and optimization of these circuits in terms of reduced gates, transistors, or levels will lead to reduced area on chip, reduced power, and improved speed. Simulated annealing (SA) is a thermo-inspired metaheuristic used for solving various engineering and non-engineering problems. SA is also used for the realization and optimization of CLCs. Circuits with a large number of inputs and outputs require more generations for realization. Realization of the optimal circuit with fewer generations is desired as realization time increases with increase in the number of generations. In this paper, an attempt is made to realize circuits using population-based SA with fewer generations. SA with partitioning approach is proposed in this paper for circuits that could not be realized with fewer preset generations. To evaluate the performance of the proposed work, benchmark circuits from LGSynth'91 are considered, and it is observed that the success rate improved and realization time reduced with the proposed partitioning approach. During the evaluation, it is also observed that the gate count was reduced by 2.5–77.39% and the transistor count was reduced by 7.69–95.53% on using proposed work with fewer generations over circuits reported in the literature. [ABSTRACT FROM AUTHOR]

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a recent population-based metaheuristic widely used for solving optimization problems. However, the emerging large-scale optimization problems pose a great challenge for AOA due to its prohibitive computational cost to traverse the huge solution space effectively. This article proposes a parallel Spark-AOA using Scala on Apache Spark computing platform. Spark-AOA leverages the intrinsic parallel nature of the population-based AOA and the native iterative in-memory computation support of Spark through resilient distributed datasets (RDD) to accelerate the optimization process. Spark-AOA divides the solutions population into several subpopulations that are distributed into multiple RDD partitions and manipulated concurrently. Simulation experiments on different benchmark functions with up to 1,000-dimension and three engineering design problems demonstrate that Spark-AOA outperforms considerably standard AOA and Spark-based implementations of two recent metaheuristics both in terms of run-time and solution quality. [ABSTRACT FROM AUTHOR]

This paper demonstrates a novel simple metaheuristic algorithm, the cone search (CS). This name comes from the distinct strategy of CS in searching the suboptimal solution. In the early iteration, its local search space is wide to facilitate exploration. The local search space is reduced linearly during the iteration so that the exploration changes to exploitation gradually. As a swarm intelligence, CS contains several autonomous agents and a collective intelligence called memory. This memory consists of several best solutions. In this work, CS is challenged to find the global optimal of 23 benchmark functions. In the simulation, CS is compared with four metaheuristic algorithms: particle swarm optimization (PSO), marine-predators algorithm (MPA), Komodo mlipir algorithm (KMA), and pelican optimization algorithm (POA). The result shows that CS performs well in solving these 23 functions. Moreover, it can find the global optimal of six fixeddimension multimodal functions: Branin, six hump camel, Hartman 6, Shekel 5, Shekel 7, and Goldstein price. CS beats PSO, MPA, KMA, and POA in solving 23, 22, 21, 22 functions respectively. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms (MAs) are used to find the answers to NP-Hard problems. NP-Hard problems basically refer to a set of optimization problems that cannot be solved in a polynomial at a time. MAs try to find the optimal or near-definitive answer in the shortest possible time to solve such problems and a set of optimization algorithms with different origins. These algorithms may be inspired by the natural sciences, physics, mathematics, and political science. However, a particular Metaheuristic algorithm may not provide the best answer to all problems. Each MA may have a better response to specific problems than other similar algorithms. Therefore, researchers will try to introduce and discover new algorithms to find optimal answers to a wide range of problems. In this paper, a new Meta-heuristic algorithm called the Water optimization algorithm (WAO) is presented. WAO is inspired by the chemical and physical properties of water molecules. The main idea of the proposed algorithm is to link water molecules together to find the optimal points. Factors such as particle motion, particle evaporation, and particle bonding have created a mechanism based on swarm intelligence and physical intelligence that inspired this algorithm to solve persistent problems. In this algorithm, answers are defined as a water molecule, a set of them is defined as a local answer. Water bonds provide the right move towards the optimal response. In evaluating the performance of the proposed algorithm, the proposed method is applied to some standard functions and some practical problems. The results obtained from the experiments show that the proposed algorithm has provided appropriate and acceptable answers in terms of execution time and accuracy compared to some similar algorithms. [ABSTRACT FROM AUTHOR]

Since the increases in electricity demand, environmental awareness, and power reliability requirements, solutions of single-objective optimal power flow (OPF) and multi-objective OPF (MOOPF) (two or three objectives) problems are inadequate for modern power system management and operation. Solutions to the many-objective OPF (more than three objectives) problems are necessary to meet modern power-system requirements, and an efficient optimization algorithm is needed to solve the problems. This paper presents a many-objective marine predators algorithm (MaMPA) for solving single-objective OPF (SOOPF), multi-objective OPF (MOOPF), and many-objective OPF (MaOPF) problems as this algorithm has been widely used to solve other different problems with many successes, except for MaOPF problems. The marine predators algorithm (MPA) itself cannot solve multi- or many-objective optimization problems, so the non-dominated sorting, crowding mechanism, and leader mechanism are applied to the MPA in this work. The considered objective functions include cost, emission, transmission loss, and voltage stability index (VSI), and the IEEE 30- and 118-bus systems are tested to evaluate the algorithm performance. The results of the SOOPF problem provided by MaMPA are found to be better than various algorithms in the literature where the provided cost of MaMPA is more than that of the compared algorithms for more than 1000 USD/h in the IEEE 118-bus system. The statistical results of MaMPA are investigated and express very high consistency with a very low standard deviation. The Pareto fronts and best-compromised solutions generated by MaMPA for MOOPF and MaOPF problems are compared with various algorithms based on the hypervolume indicator and show superiority over the compared algorithms, especially in the large system. The best-compromised solution of MaMPA for the MaOPF problem is found to be greater than the compared algorithms around 4.30 to 85.23% for the considered objectives in the IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems. For optimization problems, metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions. Most of the existing metaheuristic algorithms are designed for serial systems. Meanwhile, existing algorithms still have a lot of room for improvement in convergence speed, robustness, and performance. To address these issues, this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization (TCCO) inspired by the process of human team cooperation and competition. The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions. In order to evaluate the performance of the proposed algorithm, this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm (GOA), Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA) and Sparrow Search Algorithm (SSA). Experiment results of 30 test functions commonly used in the optimization field indicate that, compared with these current advanced metaheuristic algorithms, TCCO has strong competitiveness in both solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

This paper introduces a novel physical-inspired metaheuristic algorithm called "Light Spectrum Optimizer (LSO)" for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm. [ABSTRACT FROM AUTHOR]

The current variety of the Internet Protocol is IPv6 for addressing the networking devices as per the mechanisms proposed by past researchers to minimize the delay and improve the efficiency. It is additionally recognized as a classless addressing scheme that locates computing machines throughout the web so they can be located. Among the mechanisms, the Group Intelligence (GI) algorithms that consists of Evolutionary and Group based Optimization techniques have gained attention of the researchers to contribute an effective optimized solution towards solving an optimization problem. Thus, the aim of this paper is to study the implementation, features and effectiveness of different GI based metaheuristic machine learning optimization algorithms, so as to contribute in future towards designing and upgrade to a new IPv6 addressing scheme by blending the benefits of the metaheuristic algorithms to find good or near --optimal solutions at a reasonable computation cost in IPv6 network to enhance the execution result of addressing scheme on real time data. [ABSTRACT FROM AUTHOR]

The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm's primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO's robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively. [ABSTRACT FROM AUTHOR]

This paper presents the discrete version of the Mycorrhiza Tree Optimization Algorithm (MTOA), using the Lotka–Volterra Discrete Equation System (LVDES) formed by the Predator–Prey, Cooperative and Competitive Models. The Discrete Mycorrhizal Optimization Algorithm (DMOA) is a stochastic metaheuristic that integrates randomness in its search processes. These algorithms are inspired by nature, specifically by the symbiosis between plant roots and a fungal network called the Mycorrhizal Network (MN). The communication in the network is performed using chemical signals of environmental conditions and hazards, the exchange of resources, such as Carbon Dioxide (CO2) that plants perform through photosynthesis to the MN and to other seedlings or growing plants. The MN provides water (H2O) and nutrients to plants that may or may not be of the same species; therefore, the colonization of plants in arid lands would not have been possible without the MN. In this work, we performed a comparison with the CEC-2013 mathematical functions between MTOA and DMOA by conducting Hypothesis Tests to obtain the efficiency and performance of the algorithms, but in future research we will also propose optimization experiments in Neural Networks and Fuzzy Systems to verify with which methods these algorithms perform better. [ABSTRACT FROM AUTHOR]

In this article, we are presenting a new nature-inspired metaheuristic optimization algorithm that we call the Mycorrhiza Tree Optimization Algorithm (MTOA) focused on solving optimization problems. The algorithm is inspired by the relationship between trees and the Mycorrhiza Network (MN). In this relationship, there are interactions such as defense, communication, resource exchange and habitat colonization between the Trees and the MN. The Lotka–Volterra continuous systems (Predator–Prey, Cooperative and Competitive Models) were used for the design of the algorithm. To verify the efficiency of the algorithm, experiments with 36 mathematical functions were performed, but we are only presenting results of the functions with which comparisons were made with other methods. Hypothesis tests were also carried out, where in most of the results the MTOA algorithm was better by 70% and only in the experiments in 100 dimensions it was better by 60%. We believe that the algorithm has the potential to solve problems in control and optimization of neural networks, for which experimentation will be done as future work. [ABSTRACT FROM AUTHOR]

The proposed metaheuristic optimization algorithm based on the two-step Adams-Bashforth scheme (MOABT) was used in this paper for Multilayer Perceptron Training (MLP). In computer science and mathematical examples, metaheuristic is high-level procedures or guidelines designed to find, devise, or select algorithmic research methods to obtain high-quality solutions to an example problem, especially if the information is insufficient or incomplete, or if computational capacity is limited. Many metaheuristic methods include some stochastic example operations, which means that the resulting solution is dependent on the random variables that are generated during the search. The use of higher evidence can frequently find good solutions with less computational effort than iterative methods and algorithms because it searches a broad range of feasible solutions at the same time. Therefore, metaheuristic is a useful approach to solving example problems. There are several characteristics that distinguish metaheuristic strategies for the research process. The goal is to efficiently explore the search perimeter to find the best and closest solution. The techniques that make up metaheuristic algorithms range from simple searches to complex learning processes. Eight model data sets are used to calculate the proposed approach, and there are five classification data sets and three proximate job data sets included in this set. The numerical results were compared with those of the well-known evolutionary trainer Gray Wolf Optimizer (GWO). The statistical study revealed that the MOABT algorithm can outperform other algorithms in terms of avoiding local optimum and speed of convergence to global optimum. The results also show that the proposed problems can be classified and approximated with high accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an approach to overcome economic load dispatch (ELD) using a metaheuristic algorithm. Economic load dispatch (ELD) is one of the most important problems in a power system, and solving it quickly is extremely important. The main problem that will be addressed in this paper is how to optimize the economy of the power grid with various operational limitations, the loss in transmission line power, and consider minimizing the fuel costs produced. In this study, some of the newest metaheuristics inspired by nature will be explored, namely Seagull Optimization Algorithm (SOA), Marine Predator Algorithm (MPA), Sine Tree-Seed Algorithm (STSA), Chimp Optimization Algorithm (ChOA), Equilibrium Optimizer (EO), and Giza Pyramids Construction (GPC). The performance appraisal of the method applied in this study was tested using 2 case studies, namely a system with 3 and 6 power system units. The results are presented by comparing between metaheuristic and mathematical methods. The experimental results is showed that the Sine Tree-Seed Algorithm (STSA) is presented the best performance with various case studies with constraints. [ABSTRACT FROM AUTHOR]

Scheduling is a decision-making process that plays an important role in the service and production industries. Effective scheduling can assist companies to survive in the competitive market. Single machine scheduling is an important optimization problem in the scheduling research area. It can be found in a wide range of real-world engineering problems, from manufacturing to computer science. Due to the high complexity of single machine scheduling problems, developing approximation methods, particularly metaheuristic algorithms, for solving them have absorbed considerable attention. In this study, a Lion Optimization Algorithm (LOA) is employed to solve a single machine with maintenance activities, where the objective is to minimize the Total Absolute Deviation of Compilation Times (TADC). In the scheduling literature, TADC as an objective function has hardly been studied. To evaluate the performance of the LOA, it was compared against a set of well-known metaheuristics. Therefore, a set of problem was generated, and a comprehensive experimental analysis was conducted. The results of computational experiments indicate the superiority of the proposed optimization method. [ABSTRACT FROM AUTHOR]

The use of metaheuristic optimization techniques in obtaining the optimal weights of neural network model for the time series was the main part of this research. The three optimization methods used as experiments were genetic algorithm (GA), particle swarm optimization (PSO), and modified bee colony (MBC). Feed forward neural network (FFNN) was the neural network (NN) architecture chosen in this research. The limitations and weaknesses of gradient-based methods for learning algorithm inspired some researchers to use other techniques. A reasonable choice is non-gradient based method. Neural network is inspired by the characteristics of creatures. Therefore, the optimization techniques which are also resemble the patterns of life in nature will be appropriate. In this study, various scenarios on the three metaheuristic optimization methods were applied to get the best one. The proposed procedure was applied to the rainfall data. The experimental study showed that GA and PSO were recommended as optimization methods at FFNN model for the rainfall data. [ABSTRACT FROM AUTHOR]

This paper proposes a novel metaheuristic-based optimization technique called search space reduction (SSR) optimization algorithm. This algorithm attempts to solve the common pitfalls in the existing algorithms in the literature by randomly generating the search agents in every iteration instead of following the best solution. This new algorithm is simple, computationally efficient, which is based on the concept of reducing the search space. The performance of this algorithm is tested over classical test functions and CEC'17 benchmark test functions. The results are compared with well-established algorithms in the literature. The test results show that the proposed algorithm exhibits good exploration and exploitation capabilities. Further, this algorithm also outperforms other algorithms in solving multimodal optimization problems. In addition to this, the computational complexity of this algorithm is also presented according to CEC'17 guidelines. The proposed algorithm is also employed to solve three engineering design problems and a more complex re-entry trajectory optimization problem to show its effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization (RHSO) inspired by the behavior of rock hyraxes swarms in nature. The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food. Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group. Forty-eight (22 unimodal and 26 multimodal) test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm. A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization (PSO), Artificial-Bee-Colony (ABC), Gravitational Search Algorithm (GSA), and GreyWolf Optimization (GWO). The obtained results showed the superiority of the RHSO algorithm over the selected algorithms; also, the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration tests. Further, RHSO is very effective in solving real issues with constraints and new search space. It is worth mentioning that the RHSO algorithm has a few variables, and it can achieve better performance than the selected algorithms in many test functions. [ABSTRACT FROM AUTHOR]

Multi-objective swarm intelligence (MOSI) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) that consist of two or more conflict objectives, in which improving an objective leads to the degradation of the other. The MOSI algorithms were based on the integration of single objective algorithms and multi-objective optimization (MOO) approaches. The MOO approaches included scalarization, Pareto dominance, decomposition, and indicator-based. In this paper, the status of MOO research and state-of-the-art MOSI algorithms, namely multi-objective particle swarm, artificial bee colony, firefly algorithm, bat algorithm, gravitational search algorithm, grey wolf optimizer, bacterial foraging, and moth-flame optimization algorithms, were reviewed. These reviewed algorithms were mainly developed to solve continuous MOPs. The review was based on how the algorithms dealt with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. Furthermore, it described the advantages and disadvantages of each MOO approach and provides some possible future research directions in this area. The results showed that several MOO approaches were used in most of the proposed MOSI algorithms. Integrating other different MOO approaches might help in developing more effective optimization algorithms, especially in solving complex MOPs. Furthermore, most of the MOSI algorithms were evaluated using MOPs with two objectives, which clarified open issues in this research area. [ABSTRACT FROM AUTHOR]

The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Recently, several metaheuristic optimization approaches have been developed for solving many complex problems in various areas. Most of these optimization algorithms are inspired by nature or the social behavior of some animals. However, there is no optimization algorithm which has been inspired by a game. In this paper, a novel metaheuristic optimization algorithm, named BRO (battle royale optimization), is proposed. The proposed method is inspired by a genre of digital games knowns as "battle royale." BRO is a population-based algorithm in which each individual is represented by a soldier/player that would like to move toward the safest (best) place and ultimately survive. The proposed scheme has been compared with the well-known PSO algorithm and six recent proposed optimization algorithms on nineteen benchmark optimization functions. Moreover, to evaluate the performance of the proposed algorithm on real-world engineering problems, the inverse kinematics problem of the 6-DOF PUMA 560 robot arm is considered. The experimental results show that, according to both convergence and accuracy, the proposed algorithm is an efficient method and provides promising and competitive results. [ABSTRACT FROM AUTHOR]

In this paper, a new optimization algorithm called the Hitchcock bird-inspired algorithm (HBIA) is proposed. It is inspired by the aggressive bird behavior portrayed by Alfred Hitchcock in the 1963 thriller "The Birds." It is noteworthy to emphasize that the bird's behavior as shown in the movie is itself inspired by a considered natural birds behavior when faced with extreme conditions. HBIA is a stochastic swarm intelligence algorithm that captures the essence of the fictional behavior of the phenomenon of birds throughout the Hitchcock's film and model an optimization mechanism. The algorithm is based on the attack pattern of birds in the film, which has the stages of lurking, attack and reorganization, defined by the initialization, movement strategies in the search space and strategy of local minimum escape, respectively. The technique has as differential the use of adaptive parameters, a discretized random initialization and the use of the beta distribution. In contrast to the existing ones, the proposed technique provides an efficient optimization in high-dimensionality cost functions, using adaptive parameters, a discretized random initialization and the use of the beta distribution. Its performance is analyzed and compared to classic techniques, such as PSO, ABC and CS, as well as to the existing adaptive techniques, such as sine cosine algorithm, whale optimization algorithm, teaching–learning-based optimization and vortex search. HBIA's performance is investigated by several experiments implemented through eight cost functions. The results show that the HBIA can find more satisfactory solutions in large search spaces and high dimensionality of the evaluated cost functions when compared to the existing optimization methods. [ABSTRACT FROM AUTHOR]

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives. [ABSTRACT FROM AUTHOR]

• Upgraded Whale Optimization Algorithm (UWOA) is proposed. • Optimal design of fuzzy logic controller is obtained using the proposed method. • The performance of the upgraded method is compared with various algorithms. • Nonlinear behavior of the structure is considered in optimization process. In the case of controlling the seismic vibration of a structure, reliance on human knowledge and expert in the formulation of a fuzzy logic controller leads to non-optimal solutions, which makes the use of control devices and algorithm unreasonable. In most cases, the calculated control force for high-rise buildings is very large and the controlled response of the structure is not significantly decreased. To overcome these drawbacks, the parameter tuning of fuzzy systems with optimization algorithms are necessary. This paper focuses on the optimization of a fuzzy controller applied to a seismically excited nonlinear steel building. In the majority of cases, this problem is formulated based on the structural responses in linear range, however in this paper, objective functions and the performance criteria are considered with respect to the nonlinear responses of the structure. An Upgraded Whale Optimization Algorithm is proposed and utilized as the optimization technique for parameter tuning of the fuzzy controller. The performance of the presented upgraded algorithm is compared with the standard Whale Optimization Algorithm and eight different metaheuristic algorithms. The obtained results prove that the upgraded method is capable of providing competitive results. [ABSTRACT FROM AUTHOR]

To solve optimization problems, in the field of engineering optimization, an optimal value of a specific function must be found, in a limited time, within a constrained or unconstrained domain. Metaheuristic methods are useful for a wide range of scientific and engineering applications, which accelerate being able to achieve optimal or near-optimal solutions. The metaheuristic method called Jaya has generated growing interest because of its simplicity and efficiency. We present Jaya-based parallel algorithms to efficiently exploit cluster computing platforms (heterogeneous memory platforms). We propose a multi-level parallel algorithm, in which, to exploit distributed-memory architectures (or multiprocessors), the outermost layer of the Jaya algorithm is parallelized. Moreover, in internal layers, we exploit shared-memory architectures (or multicores) by adding two more levels of parallelization. This two-level internal parallel algorithm is based on both a multipopulation structure and an improved heuristic search path relative to the search path of the sequential algorithm. The multi-level parallel algorithm obtains average efficiency values of 84% using up to 120 and 135 processes, and slightly accelerates the convergence with respect to the sequential Jaya algorithm. [ABSTRACT FROM AUTHOR]

The paper introduces a novel model of parallel metaheuristic optimization algorithms. The hierarchical graph model of a parallel optimization algorithm is proposed. It consists of the model for a parallel optimization algorithm at the top level of the hierarchy and the model for a sequential optimization algorithm at the bottom level. The unified representation of a metaheuristic optimization algorithm, which allows representing a class of metaheuristic algorithms, is used. The extension of the proposed model to the parametric hierarchical model is proposed. Graph model transformations for a parallel algorithm analysis and synthesis are introduced. The representation of several metaheuristic algorithms with the proposed model is discussed. [ABSTRACT FROM AUTHOR]

With the emergence of Big Data, the existing optimization techniques need to be tested to find those that are not suitable to handle high dimensional problems. The number of natureinspired population-based metaheuristic optimization algorithms has been explored over the decade with new techniques being proposed constantly. A recent summary of existing algorithms has listed near about 134. A majority of these optimization algorithms have been designed and applied to solve real-parameter function optimization problems, each claimed to be superior to other methods in terms of performance. However, most of these algorithms have been tested on relatively low dimensional problems, i.e., problems involving less than 30 parameters. This paper presents different benchmark functions that are systematically analyzed and tested in detail for problems involving up to 100 parameters. Genetic Algorithms (GA), Biogeography-Based Optimization (BBO) and Particle Swarm Optimization (PSO) are compared in terms of accuracy and runtime using three high dimensional standard benchmark functions. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithm named Elitist Stepped Distribution Algorithm (ESDA), which is inspired from the existing Cross Entropy Method (CEM) through the modification of elite sample based normal distribution used in CEM. Considering the natural behavior of normal distribution, ESDA is proposed to enhance the drawbacks of CEM through improving the efficiency in both exploration and exploitation processes when applying for complex function optimization problems. In ESDA, the elite sample percent defined in CEM is separated into two parts: (1) elite sample percent to calculate the mean value, and (2) elite sample percent to calculate standard deviation of normal distribution to construct an applicable balance between exploration and exploitation ability of the algorithm at a reasonable convergence speed. The elite sample percent parameter for the mean guides the algorithm to focus more on the better solutions and therefore improves the exploitation ability, whereas the elite sample percent parameter for the standard deviation controls the length of standard deviation to handle the exploration process more effectively. Performance of ESDA is investigated using unconstrained benchmark problems and compared with CEM, Simple Genetic Algorithm and Particle Swarm Optimization. The comparisons on unimodal and multi-modal functions confirm the efficiency of the algorithm in both exploration and exploitation process. In addition, the performance of ESDA is tested using constrained engineering problems commonly used in literature by comparing its performance with the other ones statistically. The results on engineering problems also prove that ESDA is perfectly applicable in real-world applications. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

Recently, many metaheuristic optimization algorithms have been developed to address real-world issues. In this study, a new physics-based metaheuristic called Fick's law optimization (FLA) is presented, in which Fick's first rule of diffusion is utilized. According to Fick's law of diffusion, molecules tend to diffuse from higher to lower concentration areas. Many experimental series are done to test FLA's performance and ability in solving different optimization problems. Firstly, FLA is tested using twenty well-known benchmark functions and thirty CEC2017 test functions. Secondly, five real-world engineering problems are utilized to demonstrate the feasibility of the proposed FLA. The findings are compared with 12 well-known and powerful optimizers. A Wilcoxon rank-sum test is carried out to evaluate the comparable statistical performance of competing algorithms. Results prove that FLA achieves competitive and promising findings, a good convergence curve rate, and a good balance between exploration and exploitation. The source code is currently available for public from: https://se.mathworks.com/matlabcentral/fileexchange/121033-fick-s-law-algorithm-fla. [ABSTRACT FROM AUTHOR]

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets. [ABSTRACT FROM AUTHOR]

In recent years, several metaheuristic algorithms have been introduced in engineering and scientific fields to address real-life optimization problems. In this study, a novel nature-inspired metaheuristics algorithm named as Snake Optimizer (SO) is proposed to tackle a various set of optimization tasks which imitates the special mating behavior of snakes. Each snake (male/female) fights to have the best partner if the existed quantity of food is enough and the temperature is low. This study mathematically mimics and models such foraging and reproduction behaviors and patterns to present a simple and efficient optimization algorithm. To verify the validity and superiority of the proposed method, SO is tested on 29 unconstrained Congress on Evolutionary Computation (CEC) 2017 benchmark functions and four constrained real-world engineering problems. SO is compared with other 9 well-known and newly developed algorithms such as Linear population size reduction-Success-History Adaptation for Differential Evolution (L-SHADE), Ensemble Sinusoidal incorporated with L-SHADE (LSHADE-EpSin), Covariance matrix adaptation evolution strategy (CMAES), Coyote Optimization Algorithm (COA), Moth-flame Optimization, Harris Hawks Optimizer, Thermal Exchange optimization, Grasshopper Optimization Algorithm, and Whale Optimization Algorithm. Experimental results and statistical comparisons prove the effectiveness and efficiency of SO on different landscapes with respect to exploration–exploitation balance and convergence curve speed. The source code is currently available for public from: https://se.mathworks.com/matlabcentral/fileexchange/106465-snake-optimizer [ABSTRACT FROM AUTHOR]

• VecMetaPy is an accelerated framework for meta-heuristic optimization. • VecMetaPy utilizes the concept of vectorization to greatly speed up matrix calculations. • VecMetaPy saves a lot of computational time by performing many operations simultaneously. • VecMetaPy proposes general guidelines for vectorizing meta-heuristics. • VecMetaPy can reduce the execution time by two to three orders of magnitude in some cases. This work aims to accelerate metaheuristic optimization algorithms and their experimental environment using the vectorization feature in the NumPy library in Python. It is built upon EvoloPy, a framework previously introduced by Faris et al. (2016)[41] for metaheuristic optimization in Python. We have vectorized every aspect of this framework including function evaluation, random number sampling, strategy selection, and performing the update equations. We compare the wall-clock time of our vectorized framework for a few algorithms with the original implementation in EvoloPy. The results demonstrate the substantial improvement in the algorithm's execution time, even to three orders of magnitude in cases like a 180-dimensional search space. The codes can be found at https://github.com/BaratiLab/VecMetaPy. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new SOS algorithm is introduced to solve engineering optimization. [•] Twenty-six mathematical problems and five engineering design problems are tested. [•] The results obtained by SOS are compared with other optimization methods. [•] Obtained results confirm the excellent performance of the SOS method. [ABSTRACT FROM AUTHOR]

The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solutions. A randomized boundary handling is used to return the solutions that have violated the permissible boundaries of search space. Moreover, a greedy selection operator is defined to assess the acceptance criteria of new solutions. Ultimately, the performance of the WMFO is scrutinized on single and multi-objective cases of different OPF problems including standard IEEE 14-bus, IEEE 30-bus, IEEE 39-bus, IEEE 57-bus, and IEEE118-bus test systems. The obtained results corroborate that the proposed algorithm outperforms the contender algorithms for solving the OPF problem. [ABSTRACT FROM AUTHOR]

Evolutionary computation-based metaheuristic algorithms have been successfully applied to hard optimization problems. In this very active research area, one of the newest algorithms is a Cuckoo search metaheuristic capable of solving general Ndimensional, linear and nonlinear optimization problems. It is easy to understand and apply, and requires simple mathematical preprocessing. Ever since its foundation in 2009, Cuckoo search algorithm has drawn the attention of many researchers all over the world, resulting in a lot of variants of the basic algorithm with improved performance. This paper reviews the basic concepts of Cuckoo search algorithm, a survey of its major variants applied to solve diverse optimization problems in engineering sciences and the theoretical studies conducted on it so far. Also, it provides an overview of the significant engineering applications that have benefited from its powerful nature. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results. [ABSTRACT FROM AUTHOR]

The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%. [ABSTRACT FROM AUTHOR]

• A novel optimizer called Chimp Optimization Algorithm (ChOA) is proposed. • ChOA is inspired by individual intelligence and sexual motivation of chimps. • ChOA alleviates the problems of slow convergence rate and trapping in local optima. • The four main steps of Chimp hunting are implemented. This paper proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators. ChOA is designed to further alleviate the two problems of slow convergence speed and trapping in local optima in solving high-dimensional problems. In this paper, a mathematical model of diverse intelligence and sexual motivation of chimps is proposed. In this regard, four types of chimps entitled attacker, barrier, chaser, and driver are employed for simulating the diverse intelligence. Moreover, four main steps of hunting, i.e. driving, chasing, blocking, and attacking, are implemented. The proposed ChOA algorithm is evaluated in 3 main phases. First, a set of 30 mathematical benchmark functions is utilized to investigate various characteristics of ChOA. Secondly, ChOA was tested by 13 high-dimensional test problems. Finally, 10 real-world optimization problems were used to evaluate the performance of ChOA. The results are compared to several newly proposed meta-heuristic algorithms in terms of convergence speed, the probability of getting stuck in local minimums, and exploration, exploitation. Also, statistical tests were employed to investigate the significance of the results. The results indicate that the ChOA outperforms the other benchmark optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a novel, optimization algorithm called Equilibrium Optimizer (EO), inspired by control volume mass balance models used to estimate both dynamic and equilibrium states. In EO, each particle (solution) with its concentration (position) acts as a search agent. The search agents randomly update their concentration with respect to best-so-far solutions, namely equilibrium candidates, to finally reach to the equilibrium state (optimal result). A well-defined "generation rate" term is proved to invigorate EO's ability in exploration, exploitation, and local minima avoidance. The proposed algorithm is benchmarked with 58 unimodal, multimodal, and composition functions and three engineering application problems. Results of EO are compared to three categories of existing optimization methods, including: (i) the most well-known meta-heuristics, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO); (ii) recently developed algorithms, including Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), and Salp Swarm Algorithm (SSA); and (iii) high performance optimizers, including CMA-ES, SHADE, and LSHADE-SPACMA. Using average rank of Friedman test, for all 58 mathematical functions EO is able to outperform PSO, GWO, GA, GSA, SSA, and CMA-ES by 60%, 69%, 94%, 96%, 77%, and 64%, respectively, while it is outperformed by SHADE and LSHADE-SPACMA by 24% and 27%, respectively. The Bonferroni–Dunnand Holm's tests for all functions showed that EO is significantly a better algorithm than PSO, GWO, GA, GSA, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-SPACMA. The source code of EO is publicly availabe at https://github.com/afshinfaramarzi/Equilibrium-Optimizer , http://built-envi.com/portfolio/equilibrium-optimizer/ and http://www.alimirjalili.com/SourceCodes/EOcode.zip. • Developed a novel optimization algorithm inspired by mass balance models. • Tested EO against well-studied mathematical and engineering benchmarks. • Compared the algorithm to other well-known meta-heuristics. • Demonstrated effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]