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This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

The use of metaheuristics is currently on the rise for solving real problems due to their complexity and uncertainty management. Most of the current existing metaheuristic algorithms have the problem of local minima and fixed parameters. Fuzzy logic has contributed to solving this problem. It has been shown that the use of type-1 and type-2 fuzzy theory applied in parameter adaptation has contributed to effectively solve this problem. The advantage of utilizing fuzzy theory in parameter adaptation is the uncertainty management that offers a significant enhancement in finding solutions. The main goal is to utilize type-3 fuzzy theory in parameter adaptation of harmony search. Type-3 membership functions can use vertical slices for their construction. A new type-3 fuzzy harmony search approach is utilized to find the antecedent and consequent parameters of a fuzzy controller problem. Experiments with different lower scale parameters were carried out to verify the benefits of modeling the uncertainty domain with the type-3 fuzzy approach. A level of disturbance was applied to the control process to validate the performance of the method with respect to those existing in the literature. [ABSTRACT FROM AUTHOR]

Cooperation search algorithm (CSA) is a new metaheuristic algorithm inspired from the team cooperation behaviors in modern enterprises and is characterized by fast convergence. However, for complex multimodal problems, it may get trapped into local optima and suffer from premature convergence for the shortcoming of population updating guided only by leading individuals. In this paper, the issue of low convergence efficiency and convergence accuracy of the CSA algorithm on complex multimodal problems is dramatically alleviated by integrating the mutation and crossover operators in DE algorithm. Experimental results demonstrate the better performance of CCSA on convergence speed and accuracy as compared to other existing optimizers. Furthermore, aiming at the problem that there is no universal approach for the multi-degree reduction in Ball Bézier surfaces under different interpolation constrains, we propose a new method to solve this problem by introducing metaheuristic methods, where the change of interpolation constrains is treated as the change of decision variables. The modeling examples show that the proposed method is effective and easy to implement under different interpolation constrains, which can achieve the multi-degree reduction in Ball Bézier surfaces at one time and can simplify the degree reduction procedure significantly. [ABSTRACT FROM AUTHOR]

Clustering is a technique of grouping the data objects into clusters. Many metaheuristic algorithms based on swarm intelligence, physic laws, and chemical reactions, among others, have been developed for clustering. In this study, an enhanced whale optimization algorithm (EWOA) is introduced to solve clustering problems. The whale optimization algorithm (WOA) is adapted and enhanced with two additional operational procedures. The position update equations from the water wave optimization algorithm are incorporated into the algorithm to improve the search space and accelerate the convergence rate. The tabu and neighbourhood search mechanisms were added to handle the local optima situation. The efficiency of the proposed EWOA is measured using a simulation-based experiment conducted on eight benchmark datasets, and the results obtained are then compared to seven existing clustering algorithms/techniques. The performance of each algorithm is compared and analyzed using the average intra-cluster distance and f-measure parameters. The experimental results demonstrated the applicability and feasibility of the enhancements that were made and proved the superiority of the proposed EWOA clustering algorithm. [ABSTRACT FROM AUTHOR]

The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents. [ABSTRACT FROM AUTHOR]

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. [ABSTRACT FROM AUTHOR]

The rapid growing number of web services has posed new challenges for service composition computing. How to combine services that meet the needs of users in the least amount of time from a huge number of candidate services is a hot topic of research today. As a meta-heuristic algorithm for solving optimization problems, salp swarm algorithm (SSA) has been widely applied to case scenarios in different fields due to its simple structure and high performance. However, QoS-aware service composition is a discrete problem and existing methods are not suitable for it. Therefore, in this paper, we propose an improved SSA integrating chaotic mapping method for QoS service composition selection, named CSSA. Through the randomness and ergodicity of chaos, reducing the possibility of falling into local optimum and strengthening the exploitation capability of the algorithm. In addition, a fuzzy continuous neighborhood search method is used to enhance the local search capability of the algorithm which makes the discrete space of service composition in a way similar to continuous space. Finally, two well-known datasets are used to verify the effectiveness of CSSA compared to three advanced algorithms and original SSA. The test results demonstrate that CSSA has significant advantages and it also has satisfactory performance in large scale scenarios. [ABSTRACT FROM AUTHOR]

This research uses a new method called the Modified Adolescent Identity Search Algorithm (MAISA) to solve optimization problems. The identity of a teenager is a different combination of thoughts, beliefs, and abilities that are obtained by him. He communicates with other people in society and tries to use the positive or negative feedback from these social interactions to shape his identity and become an independent person. Mathematical simulation of how adolescents’ personality is formed during the mentioned process causes the emergence of this algorithm. The purpose of this research is to improve the performance of the AISA algorithm in the balance between exploration and exploitation. This algorithm isn’t trapped in the optimal local points due to the appropriate convergence rate and reaches the universal optimal answer quickly. One of the features of this algorithm that we can mention is its simplicity and having few setting parameters. To show the effectiveness of this algorithm, results of solving two types of problems of mathematical functions and practical examples such as large-scale truss 120, 244, 942, and 72-bar truss, are measured by other similar algorithms. The results show that this algorithm has a good performance in the optimal solution of such problems, compared to other similar algorithms. [ABSTRACT FROM AUTHOR]

Optimization techniques are widely used to solve variety of problems related to the fields of engineering, statistics, finance, etc. In this article, a new optimization algorithm named criminal search optimization algorithm (CSOA) has been proposed. This proposed algorithm is inspired by policemen and replicates the strategies and intelligence used by a team of the policemen to catch a criminal for a crime. The performance of CSOA is validated using two suites of benchmark functions (CEC-2005 and CEC-2020). Further, the proposed method is used to solve a multi-objective real-world optimization problem, i.e. a combined emission economic dispatch problem. To evaluate the performance of the proposed method, five test cases have been considered in this study. The results obtained are compared with other existing well-known optimization methods to show the superiority of the proposed CSOA method. [ABSTRACT FROM AUTHOR]

The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach; two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions. [ABSTRACT FROM AUTHOR]

The traffic light cycle optimization problem (TLCOP) is certainly one of the most critical problems in a modern traffic management system because a "good solution" will be able to reduce the total waiting time of vehicles on roads of an entire city. In addition to heuristic algorithms, metaheuristic algorithms provide an alternative way for solving this optimization problem in the sense that they provide a better solution to adjust the traffic lights to mitigate the traffic congestion problem. However, there is still plenty of room for improvement. One of the open issues is that most metaheuristic algorithms will converge to a few regions at the later stage of the convergence process and thus are likely to fall into local optima. The proposed algorithm—a hybrid heuristic algorithm called grey wolf with grasshopper optimization (GWGO)—is developed to leverage the strength of grey wolf and grasshopper optimization algorithms. The underlying idea is to use the grey wolf optimization to avoid falling into local optimum too quickly while using the grasshopper optimization to dynamically adjust the convergence speed of the search algorithm. The experimental results show that the proposed algorithm is able to find out better results than all the other state-of-the-art search algorithms for the TLCOP evaluated in this study in terms of the quality of the end results. [ABSTRACT FROM AUTHOR]

Since real-world problems are quite complex, developing more effective methods to optimize them is taken into consideration at the present age. One of the most popular strategies to solve such problems is nature-inspired metaheuristic algorithms which have presented promising solutions in different fields. The JAYA algorithm is an innovative and promising technique for solving a wide range of optimization problems. Hence, this investigation provides a new hybrid algorithm based on JAYA and crow search algorithm (CSA), known as HJCSA, to enhance its performance. In other words, the mechanism of updating position in CSA is integrated with JAYA algorithm to reinforce the convergence and search capabilities of JAYA algorithm. The proposed hybrid algorithm is evaluated through solving 20 well-known benchmark functions, and obtained results are compared with various up-to-date algorithms, including CSA, JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO), and sine–cosine algorithm (SCA). In addition, a real-world engineering problem is optimized by the proposed optimization approach and compared with other published works. The results showed that the suggested optimization algorithm is much more vigorous in escaping from local minima, possessing better convergence, and discovering more accurate solutions. [ABSTRACT FROM AUTHOR]

A major issue while conceiving or parameterizing an optimization heuristic is to ensure an appropriate balance between exploitation and exploration of the search. Evolution strategies and neighborhood-based metaheuristics constitute relevant high-level frameworks, which ease the problem solving but are often complex to configure. Moreover, their effective behavior, according to the particularities of the search landscapes, remains difficult to grasp. In this paper, we deeply investigate the sampled walk search algorithm, which is a local search equivalent of the (1 , λ) -evolution strategy, considering that the neighborhood relation describes mutation possibilities. We specifically designed experiments to better understand the behavior of such a strategy offering a fine way to deal with the exploration versus exploitation dilemma. The main contribution is the analysis of search trajectories by evaluating and visualizing both their width (exploration) and their height (exploitation). More generally, we aim at bringing insights about the behavior of the (1 , λ) -ES in a discrete optimization context and within a fitness landscape perspective. [ABSTRACT FROM AUTHOR]

Pathfinder algorithm (PFA) for finding the best food area or prey based on the leadership of collective action in animal groups is a new metaheuristic algorithm for solving optimization problems with different structures. PFA is divided into two stages to search: pathfinder stage and follower stage. They represent the exploration phase and mining phase of PFA respectively. However, the original algorithm also has the problem of falling into a local optimum. In order to solve this problem, the teaching phase in the teaching and learning algorithm is added to the pathfinder stage in the text. In order to balance the exploration and mining capabilities of the algorithm, the learning phase of the teaching and learning algorithm is added to the follower phase in the article. In order to further enhance the depth search ability of the algorithm and increase the convergence speed, the exponential step is given to the followers. Therefore, a teaching-learning-based pathfinder algorithm (TLPFA) is proposed. 19 benchmark functions of four different types and six engineering design problems are used to test of the TLPFA exploration and exploiting capabilities. The experimental results show that the proposed TLPFA algorithm is superior to the state-of-the-art metaheuristic algorithms in terms of the performance measures. [ABSTRACT FROM AUTHOR]

The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the Gravitational Search Algorithm (GSA) and Chaotic Gravitational Search Algorithm (CGSA) algorithms, called respectively Memetic Gravitational Search Algorithm (MGSA) and Memetic Chaotic Gravitational Search Algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic Algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm. [ABSTRACT FROM AUTHOR]

In this article, a new hybrid algorithm is proposed which was based on the elephant herding optimization (EHO) and cultural algorithm (CA), known as elephant herding optimization cultural (EHOC) algorithm. In this process, the belief space defined by the cultural algorithm was used to improve the standard EHO. EHO is motivated by herding behavior of the elephant groups. These behaviors are modeled into two operators including clan updating operator and separating operator. In EHOC, based on belief space, the separating operator is defined, which is able to create new local optimums in search space, to improve the algorithm search ability and to create an algorithm with an optimal exploration–exploitation balance. The CA, EHO, and EHOC algorithms are applied to eight mathematical optimization problems and four truss weight minimization problems, and to assess the performance of the proposed algorithm, the results are compared. The results clearly indicate that EHOC is capable of accelerating the convergence rate effectively and can develop better solutions compared to the CA and EHO. In addition, it can produce competitive results in comparison with other metaheuristic algorithms in the literature. [ABSTRACT FROM AUTHOR]

Symbiotic organisms search algorithm is a new meta-heuristic algorithm based on the symbiotic relationship between the biological which was proposed in recent years. In this paper, a novel complex-valued encoding symbiotic organisms search (CSOS) algorithm is proposed. The algorithm introduces the idea of complex coding diploid. Each individual is composed of real and imaginary parts and extends the search space from one dimension to two dimensions. This increases the diversity of the population, further enhances the ability of the algorithm to find the global optimal value, and improves the precision of the algorithm. CSOS has been tested with 23 standard benchmark functions and 2 engineering design problems. The results show that CSOS has better ability of finding global optimal value and higher precision. [ABSTRACT FROM AUTHOR]

Local search metaheuristics (LSMs) are efficient methods for solving hard optimization problems in science, engineering, economics and technology. By using LSMs, we could obtain satisfactory resolution (approximate optimum) in a reasonable time. However, it is still very CPU time-consuming when solving large problem instances. As graphic process units (GPUs) have been evolved to support general purpose computing, they are taken as a major accelerator in scientific and industrial computing. In this paper, we present an optimized parallel iterated local search algorithm efficiently accelerated on GPUs and test the algorithm with a typical case study of the Travelling Salesman Problem (TSP) in computational science. We introduce novel methods as follows: first, we present an efficient mapping between a neighborhood and a GPU thread. Second, we use the Roofline model to analyze the performance of existing GPU-based 2-opt kernels. Based on our analysis, we point out the limiting factor of these 2-opt kernels and provide our optimization approaches. Furthermore, we test our algorithm with standard TSP problem instances up to 4461 cities, in which our strategy leads to a speedup factor 279 $$\times $$ over the sequential counterpart. We compare our approach with existing high-performance GPU-based local search algorithms, and the results demonstrate that the proposed algorithm is competitive. [ABSTRACT FROM AUTHOR]

As a typical, discrete, and NP-hard problem, assembly sequence planning (ASP) has direct impact on assembly quality and costs. So far, lots of graph-based and meta-heuristic approaches cannot solve this problem effectively. This paper proposes an effective ASP algorithm based on the harmony search (HS) algorithm, which has an outstanding global search ability to obtain the global optimum more efficiently. To solve the ASP problem, an improved harmony search (IHS) algorithm is proposed mainly in four aspects: (1) an encoding of harmony is designed based on ASP problems; (2) an initial harmony memory (HM) is established using the opposition-based learning (OBL) strategy; (3) a particular way to improvise a new harmony is developed; and (4) a local search strategy is introduced to accelerate the convergence speed. Finally, the advantage of the proposed ASP algorithm over the competing algorithms in solving ASP problems is verified by two experiments. Moreover, the proposed ASP algorithm is applied to a practical problem, a propeller of azimuth thruster to verify its practicability. [ABSTRACT FROM AUTHOR]

Two new approaches for the solution of the $$p$$ -median problem in the plane are proposed. One is a Variable Neighborhood Search and the other one is a concentric search. Both approaches are enhanced by a front-end procedure for finding good starting solutions and a decomposition heuristic acting as a post optimization procedure. Computational results confirm the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A hybridisation of a clustering-based technique and of a variable neighbourhood search (VNS) is designed to solve large-scale $$p$$ -median problems. The approach is based on a multi-stage methodology where learning from previous stages is taken into account when tackling the next stage. Each stage is made up of several subproblems that are solved by a fast procedure to produce good feasible solutions. Within each stage, the solutions returned are put together to make up a new promising subset of potential facilities. This augmented $$p$$ -median problem is then solved by VNS. As these problems used aggregation, a cost evaluation based on the original demand points instead of aggregation is computed for each of the 'aggregation'-based solution. The one yielding the least cost is then selected and its chosen facilities included into the next stages. This multi-stage process is repeated several times until a certain criterion is met. This approach is enhanced by an efficient way to aggregate the data and a neighbourhood reduction scheme when allocating demand points to their nearest facilities. The proposed approach is tested, using various values of $$p$$ , on the largest data sets from the literature with up to 89,600 demand points with encouraging results. [ABSTRACT FROM AUTHOR]

This study aims to produce a novel optimization algorithm, called the Cuckoo Search Algorithm (CS), for solving the genome sequence assembly problem. Assembly of genome sequence is a technique that attempts to rebuild the target sequence from the collection of fragments. This study is the first application of the CS for DNA sequence assembly problem in the literature. The algorithm is based on the levy flight behaviour and brood parasitic behaviour. The CS algorithm is employed to maximize the overlap score by reconstructing the original DNA sequence. Experimental results show the ability of the CS to find better optimal genome assembly. To check the efficiency of the proposed technique the results of the CS is compared with one of the well known evolutionary algorithms namely, particle swarm optimization (PSO) and its variants. [ABSTRACT FROM AUTHOR]

In this paper, we present an improved and discrete version of the Cuckoo Search (CS) algorithm to solve the famous traveling salesman problem (TSP), an NP-hard combinatorial optimisation problem. CS is a metaheuristic search algorithm which was recently developed by Xin-She Yang and Suash Deb in 2009, inspired by the breeding behaviour of cuckoos. This new algorithm has proved to be very effective in solving continuous optimisation problems. We now extend and improve CS by reconstructing its population and introducing a new category of cuckoos so that it can solve combinatorial problems as well as continuous problems. The performance of the proposed discrete cuckoo search (DCS) is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that DCS is superior to some other metaheuristics. [ABSTRACT FROM AUTHOR]