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Hydraulic turbine control system is a complex system with strong nonlinearity and multiple variables. Therefore, in order to better control the turbine system, it is necessary to obtain the parameters of key components. Aiming at the limitation of the traditional particle swarm optimization algorithm in global search ability, mutation operator and dynamic inertia weight coefficient are introduced to enhance the search ability of the algorithm. In addition, in order to further improve the global search performance of the algorithm, this paper combines the optimized particle swarm optimization algorithm with genetic algorithm to form a hybrid parameter identification algorithm. The hybrid algorithm not only uses the fast convergence of particle swarm optimization (PSO), but also uses the global search advantage of genetic algorithm (GA) to realize efficient and accurate identification of turbine torque and load parameters. Through MATLAB2021a/Simulink simulation experiments, the application effect of the algorithm in the identification of turbine torque and generator load parameters is verified. The simulation results show that the optimized particle swarm optimization algorithm has significant advantages in the accuracy and robustness of parameter identification, and the identified parameters have a high degree of fitting with the actual measured torque and speed. This study not only provides a new optimization strategy for the parameter identification of turbine regulation system, but also provides an effective intelligent algorithm solution for the parameter identification of nonlinear system. [ABSTRACT FROM AUTHOR]

The fixed-charge transportation (FCT) problem, an extension of the classical transportation problem, holds significance in practical logistics scenarios where fixed charges play a crucial role. Fixed charges categorize it as an NP-hard problem, posing challenges for conventional methods due to their inefficiency, high computational costs, and susceptibility to local optima. This paper introduces an enhanced evolutionary algorithm, offline learning-based competitive swarm optimization (OLCSO), tailored to address the FCT problems efficiently. OLCSO draws inspiration from offline learning, where particles benefit from the experiences of high performers. The algorithm incorporates (i) a ranking-based mechanism where losers learn from peer-to-peer winners based on their ranks, (ii) a two-phase cooperative evolutionary mechanism enhancing searchability and convergence of the algorithm. In the first phase, losers learn from personal best experiences and the shifted center of the population, boosting diversity. In the second phase, losers learn from winners and the center of the population, improving exploitation, (iii) a mutation strategy is embedded to update winner locations in each evolutionary procedure, and (iv) to ensure a feasible solution to FCT problems, the paper also introduces negative and fractional repair mechanisms. OLCSO is applied to solve two sets of FCT problems, linear and non-linear. Experimental results are compared with various heuristic and metaheuristic techniques, commonly used for these problems, using diverse metrics. A comparative analysis of OLCSO's design elements is also conducted. The results demonstrate OLCSO's superiority over existing algorithms. [ABSTRACT FROM AUTHOR]

In recent years, there have been notable advancements in the theoretical research of existing meta-heuristic algorithms and in the development of novel algorithms. However, the research on the definition and construction methods of the so-called "mutation" operator, a concept widely used in existing meta-heuristic algorithms, remains notably insufficient. In addition, how to directly construct a meta-heuristic algorithm under the guidance of stability and invariance analysis continues to pose a challenge that has yet to resolve. This paper carefully investigates and rigorously defines the concepts of "mutation operator" and "mutation-driven meta-heuristic algorithm" as found in the literature. According to these definitions, a novel meta-heuristic algorithm, called Multi-direction guided Mutation-driven Stable swarm Intelligence algorithm with Translation and Rotation invariance (M2SITRI) is proposed, and the corresponding mutation operator is carefully designed based on the analysis of translation invariance, rotation invariance, order-1 stability, and order-2 stability. The stability and invariance properties of the M2SITRI algorithm are validated through numerical simulations, and its superior solving performance is demonstrated by benchmarking it against several other well-known existing meta-heuristic algorithms across a variety of test problems. • Mutation operator and mutation-driven meta-heuristic algorithm concepts are defined. • A novel translation and rotationally invariant meta-heuristic algorithm termed M2SITRI is proposed. • The invariance and stability properties and the excellent solving performance of M2SITRI algorithm are verified. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel adaptive differential evolution algorithm for global optimization problems by introducing an enhanced mutation operator and a new mixed control parameter setting based on dual performance evaluation metrics. To further strengthen the search efficiency of algorithm and availably balance its exploration and exploitation, a dual performance metrics-based mutation operator is first proposed to implement the search of population by organically integrating the fitness value and history update of individual to find the potential promising areas and allocate the suitable search resources for them. Meanwhile, a dual performance metrics-based mixed parameter setting is developed to yield appropriate associated parameters for each individual by comprehensively measuring its search characteristic and requirement based on both its fitness value and history update. In addition, a new restart strategy is further put forward to boost the search performance of algorithm by reasonably replacing the meaningless individuals measured by both their fitness values and history updates with the randomly generated individuals based on Gaussian walk. In contrast to the existing DE versions, the new algorithm organically takes advantage of the fitness value and history update of individual to assign the proper computational resources for each potential promising region, create the suitable parameters for different individuals, and eliminate the valueless individuals from population. Thereby, it is capable of heightening the search efficiency of algorithm and maintaining a good balance between exploration and exploitation effectively. At last, the performance of the proposed algorithm is evaluated by comparing with 19 typical or up-to-date algorithms on 42 benchmark functions from both IEEE CEC2017 and CEC2022 test suites. Compared to these opponents, the proposed algorithm achieves significantly better performance on 60 out of 77 cases based on the multiproblem Wilcoxon signed-rank test at a significant level of 0.05, and thus is a more promising optimizer. [ABSTRACT FROM AUTHOR]

In this article, a series of learning strategies are proposed to enhance the optimization ability of the artificial electric field algorithm. Orthogonal learning is an important mathematical tool that can greatly influence the adaptability of population-based optimization algorithms. This article proposes, (i) an orthogonal array-based learning strategy to generate a better initial population for the artificial electric field algorithm. Along with the changes in the initialization mechanism, this article also proposes, (ii) an archive-based self-adaptive learning strategy for an artificial electric field algorithm. The proposed learning strategy divides the population into ordinary and extraordinary sub-populations, each with distinct learning mechanisms. The ordinary sub-population utilizes six learning strategies based on three archives, which contain individuals of different quality levels. We incorporate, (iii) a mutation strategy also to update the extraordinary sub-population. Finally, (iv) a self-adaptive strategy is implemented to dynamically adjust the parameters of the proposed algorithm. The effectiveness of these mechanisms is assessed through an extensive analysis of exploration–exploitation dynamics and diversity. Furthermore, an independent structural study is conducted to examine the impact of implemented mechanisms on the algorithm's behavior and efficiency. The proposed algorithm is evaluated on real parameter CEC 2017 problems across different dimensional search spaces. It is compared to eleven state-of-the-art algorithms, and the results demonstrate superior performance in terms of solution accuracy, convergence rate, search capability, and stability. The overall ranking highlights its exceptional potential for solving challenging optimization problems. Additionally, it outperforms other state-of-the-art algorithms across various dimensions, achieving accuracy rates of 64.48%, 70.05%, 78.73%, and 79.25% for dimensions 10, 30, 50, and 100, respectively. Furthermore, it demonstrates superior performance, outperforming others in 73.13% and 60.61% of the problems concerning average accuracy and statistical significance across all dimensions, respectively. [Display omitted] • Introducing an archive-based self-adaptive artificial electric field algorithm (AEFA) with orthogonal initialization for real-parameter optimization problems. • Proposal of an orthogonal array into the AEFA to initialize the population along with a self-adaptive parameter mechanism. • A novel strategy is proposed to divide the population into two homogeneous sub-populations. • Introduction of six learning strategies based on three elite archives and utilizing mutation mechanism. • A rigorous analysis of the proposed algorithm in terms of diversity, exploration–exploitation, convergence, and statistical validation. [ABSTRACT FROM AUTHOR]

In this study, we consider the scenario that differential evolution (DE) is applied for global numerical optimization and the index-based neighborhood information of population is used for enhancing the performance of DE. Although many methods are developed under this scenario, neighborhood information of current population has not been systematically exploited in the DE algorithm design. Furthermore, previous studies have shown the effect of neighborhood topology interacted with the function being solved. However, there are few investigations of DE that consider different topologies for different functions during the evolutionary process. Motivated by these observations, a new DE framework, named neighborhood-adaptive DE (NaDE), is presented. In NaDE, a pool of index-based neighborhood topologies is firstly used to define multiple neighborhood relationships for each individual and then the neighborhood relationships are adaptively selected for the specific functions during the evolutionary process. In this way, a more appropriate neighborhood relationship for each individual can be determined adaptively to match different phases of the search process for the function being solved. After that, a neighborhood-dependent directional mutation operator is introduced into NaDE to generate a new solution with the selected neighborhood topology. Being a general framework, NaDE is easy to implement and can be realized with most existing DE algorithms. In order to test the effectiveness of the proposed framework, we have evaluated NaDE via investigating several instantiations of it. Experimental results have shown that NaDE generally outperforms its corresponding DE algorithm on different kinds of optimization problems. Moreover, the synergy among different neighborhood topologies in NaDE is also revealed when compared with the DE variants with single neighborhood topology. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population-based stochastic search algorithm. This algorithm is utilized to solve the optimization problem. It suffers the problems of trapping in local optimum and premature convergence. Nowadays, both problems can be solved by mutation and reposition techniques apply with PSO (MRPSO). However, this technique use over evaluation calls for solution searching. This research paper proposed a technique to minimized evaluation call. This proposed is called DPM-MRPSO. The concept of adaption evaluation call depends on results from improvement solution of mutation technique and PSO technique. The proposed technique is tested on twenty-four benchmark functions and obtains satisfied search results. [ABSTRACT FROM AUTHOR]

Biogeography-based optimization (BBO) is a swarm intelligence optimization algorithm based on migration and mutation operations, which is usually used to solve the complex optimization problems. However, it is also a challenging task for conventional BBO to solve some complex and diversified optimization problems with a perfect balance between the performance of exploration and exploitation. In this paper, we propose a variant of BBO approach called BBO with improved migration and adaptive mutation (BBOIMAM) to improve the performance of conventional BBO for dealing with different optimization problems. First, BBOIMAM introduces an improved migration strategy which includes the generalized sinusoidal migration model and immigration strategy based on elite-learning mechanism for the improvement of the local search ability. Second, we propose an adaptive mutation strategy based on the spring vibration to further enhance the population diversity, so that improving the global search ability of the algorithm. By the combination of the proposed improved migration and adaptive mutation strategies, the exploration and exploitation performance of the algorithm can be balanced. We make a large number of experiments on a set of various kinds of benchmark functions, and the experimental results demonstrate that the proposed BBOIMAM approach achieves better performance than several state-of-the-art peer algorithms on CEC 2017 and CEC 2020 test function sets and three cases of the antenna array beam pattern optimization problems. • A BBO with improved migration and adaptive mutation (BBOIMAM) is proposed. • An improved migration strategy based on the sinusoidal and elite-learning is proposed. • An adaptive mutation strategy based on spring vibration is proposed. • BBOIMAM has the overall best performance on test functions and real applications. [ABSTRACT FROM AUTHOR]

There are many optimization heuristics which involves mutation operator. Reducing them to binary optimization allows to study properties of binary mutation operator. Modern heuristics yield from Lévy flights behavior, which is a bridge between local search and random shooting in binary space. The paper is oriented to statistical analysis of binary mutation with Lévy flight inside and Quantum Tunneling heuristics. [ABSTRACT FROM AUTHOR]

Abstract: In radio frequency identification (RFID) systems, communication signals from one desired reader is subject to interference from the other adjacent readers operating at the same time, so the reader-to-reader collision problem occurs. Many RFID reader collision avoidance methods have been developed, such as coverage-based methods, control mechanism-based methods and scheduling-based methods. In the scheduling-based methods, how to allocate frequency channels and time slots for the RFID reader network is emphasized. In this case, the RFID reader collision avoidance problem is transferred as an optimal scheduling problem, which can be solved by analytical methods and intelligent algorithms. Artificial Immune System (AIS) optimization is an emerging heuristic method derived from the human immune system. Due to its powerful global searching capability, AIS has been widely applied to scientific and engineering problems. This paper attempts to formulate the reader-to-reader collision problem (R2RCP) and its scheduling-based reader-to-reader collision avoidance model (R2RCAM), and proposes an improved AIS optimization for resource allocation (RA-AIS) in R2RCAM. Within the proposed RA-AIS optimization, the candidate antibody is constructed by using frequency channels and time slots, and in the mutation phase, the candidate antibody evolves dynamically according to its corresponding readers’ interfering power. The proposed RA-AIS optimization is examined on a series of numerical experiments to evaluate the effects of time slots, frequency channels, and transmitting power. Moreover, a group of comparative experiments are also arranged. The experimental results demonstrate that the proposed RA-AIS optimization is an effective method for R2RCAM, and performs better in searching the maximum interrogation area than other algorithms, such as random method (RM), genetic algorithm (GA), particle swarm optimization (PSO) and the canonical artificial immune system optimization (opt-aiNet). [Copyright &y& Elsevier]

This study presents an evolutionary neural fuzzy system (NFS) for nonlinear system control. The proposed NFS model uses functional link neural networks (FLNNs) as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. A learning algorithm, which consists of structure learning and parameter learning, is presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the particle swarm optimization (PSO) algorithm, can adjust the shape of the membership function and the corresponding weighting of the FLNN. The distance-based mutation operator, which strongly encourages a global search giving the particles more chance of converging to the global optimum, is introduced. The simulation results have shown the proposed method can improve the searching ability and is very suitable for the nonlinear system control applications. [ABSTRACT FROM AUTHOR]

Abstract: Canonical genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the shortcomings, an adaptive niche hierarchy genetic algorithm(ANHGA) is proposed. The algorithm is based on the adaptive mutation operator and crossover operator that adjusts the crossover rate and frequency of mutation of each individual, and adopts the gradient of the individual to decide their mutation value. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization. [Copyright &y& Elsevier]

Abstract: Mining the interesting function from the very complex and large data sets is an important research direction in DM and KDD. Traditional methods depend on experience and professional knowledge and involve subjectivity and blindness when determine function type and can not work well with complex function expression. Gene expression programming (GEP) is an adaptive genotype/phenotype evolutionary algorithm. Function mining method basted on GEP can overcome the shortcomings of the traditional methods. To overcome the deficiencies of GEP in maintaining population diversity and protection the optimal solution, we propose an improved gene expression programming algorithm, called GEP-FM. The new features implemented in GEP-FM are: a new gene structure adopted head, body, tail; self-adaptive mutation operator and a new fitness function. GEP-FM was applied to difficult identification function mining problems. The results show that GEP-FM has good performance and is superior to traditional algorithm and original GEP, the function mined by GEP-FM has a higher fitted value and prediction accuracy. [Copyright &y& Elsevier]

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Abstract: Economic dispatch (ED) problem is a nonlinear and non-smooth optimization problem when valve-point effects, multi-fuel effects and prohibited operating zones (POZs) have been considered. This paper presents an efficient evolutionary method for a constrained ED problem using the new adaptive particle swarm optimization (NAPSO) algorithm. The original PSO has difficulties in premature convergence, performance and the diversity loss in optimization process as well as appropriate tuning of its parameters. In the proposed algorithm, to improve the global searching capability and prevent the convergence to local minima, a new mutation is integrated with adaptive particle swarm optimization (APSO). In APSO, the inertia weight is tuned by using fuzzy IF/THEN rules and the cognitive and the social parameters are self-adaptively adjusted. The proposed NAPSO algorithm is validated on test systems consisting of 6, 10, 15, 40 and 80 generators with the objective functions possessing prohibited zones, multi-fuel effects and valve-point loading effects. The research results reveal the effectiveness and applicability of the proposed algorithm to the practical ED problem. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a simple and efficient global optimization algorithm. Benefitting from its concise structure and strong search ability, DE has been widely used in various fields. Generally, the convergence performance of DE largely depends on its mutation operation. Meanwhile, individuals' positions, which are selected as base vectors or making up difference vectors, are very important in mutation strategy. In this paper, we propose a differential evolution algorithm with both fitness and diversity ranking-based mutation operator (FDDE). Different from methods that use fitness as the only index to measure the quality of individuals, FDDE aims to assign suitable position for each individual in the mutation strategy by together considering both individuals' fitness and their diversity contribution. Firstly, a new method of estimating the individual diversity by fitness values has been proposed. Then, each individual's fitness ranking and diversity contribution are considered together to calculate a newly defined individual's final ranking. Finally, the final ranking are used in the mutation strategy. The newly improved mutation operator could be integrated with any classical or advanced DE variants with little additional time or space complexity. The proposed FDDE is compared with some DE variants based on numerical experiments over the CEC (Congress on Evolutionary Computation) 2005 benchmark sets, CEC 2013 benchmark sets and CEC 2014 benchmark sets. Experimental results clearly indicate that FDDE performs better on most test functions and improves the convergence performance of its competitors of jDE, rank-jDE, advanced SHADE, rank-SHADE and L -SHADE in both low and high dimensional problems. [ABSTRACT FROM AUTHOR]

Combining neighborhood utilization technique (NUT) has shown a tremendous benefit to differential evolution (DE) due to that the acquired neighborhood information of population is of great help in guiding the search. However, in most NUT-based DE algorithms, on the one hand, the neighborhood relationships between individuals cannot be effectively and properly learned, and on the other hand, the search roles of different individuals have not yet been fully considered in the design of the NUT. Therefore, this study develops a novel NUT, termed self-organizing neighborhood (SON), with three features: 1) the neighborhood relationships between individuals are incrementally learned and extracted by self-organizing map with the cosine similarity; 2) the neighborhood sizes for different individuals are adaptively adjusted according to their distinct roles in the search; 3) the evolution direction constructed with the neighborhood of each individual is incorporated into the mutation process to guide the search. By combining SON with DE, a SON-based DE (SON-DE) framework is proposed for global optimization. Experimental results on 58 real-parameter functions and 17 real-world problems have demonstrated the superiority of SON-DE in comparison with several state-of-the-art DE algorithms and evolutionary algorithms (EAs). [ABSTRACT FROM AUTHOR]