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The random vector functional link (RVFL) neural network has shown the potential to overcome traditional artificial neural networks' limitations, such as substantial time consumption and the emergence of suboptimal solutions. However, RVFL struggles to provide comprehensive insights into its decision-making processes. We propose the Neuro-fuzzy RVFL (NF-RVFL) model by combining RVFL with neuro-fuzzy system. The proposed NF-RVFL model takes humanlike decisions based on the IF-THEN approach and enhances its transparency in decision-making. Within this framework, input features undergo a fuzzification process as they traverse the fuzzy layer. The resulting fuzzified features then navigate a hidden layer through random projection as well as yielding defuzzified values via defuzzification. The defuzzified values, hidden layer outputs and original input features collectively contribute to the output prediction process. The proposed NF-RVFL model employs three distinct clustering methods to establish fuzzy layer centers: randomly initialized centers (referred to as R-means), K-means clustering centers, and fuzzy C-means clustering centers. This approach generates three distinct model variations, namely NF-RVFL-R, NF-RVFL-K and NF-RVFL-C, each producing a diverse set of fuzzified and defuzzified samples. Our research involves experiments on various UCI benchmark datasets, covering binary, multiclass classification, and regression tasks. The statistical tests and comprehensive experimental analyses consistently show that all variations of the proposed NF-RVFL model outperform baseline models, highlighting their generalization capabilities. The proposed NF-RVFL models show the generic nature by being adeptly applicable and excelling in regression as well as classification tasks.

This work addresses multiobjective dynamic surgery scheduling problems with considering uncertain setup time and processing time. When dealing with them, researchers have to consider rescheduling due to the arrivals of urgent patients. The goals are to minimize the fuzzy total medical cost, fuzzy maximum completion time, and maximize average patient satisfaction. First, we develop a mathematical model for describing the addressed problems. The uncertain time is expressed by triangular fuzzy numbers. Then, four meta-heuristics are improved, and eight variants are developed, including artificial bee colony, genetic algorithm, teaching-learning-base optimization, and imperialist competitive algorithm. For improving initial solutions’ quality, two initialization strategies are developed. Six local search strategies are proposed for fine exploitation and a $Q$ -learning algorithm is used to choose the suitable strategies among them in the iterative process of the meta-heuristics. The states and actions of $Q$ -learning are defined according to the characteristic of the addressed problems. Finally, the proposed algorithms are tested for 57 instances with different scales. The analysis and discussions verify that the improved artificial bee colony with $Q$ -learning is the most competitive one for scheduling the dynamic surgery problems among all compared algorithms.

Many real-life problems can be formulated as numerical optimization problems. Such problems pose a challenge for researchers when designing efficient techniques that are capable of finding the desired solution without suffering from premature convergence. This paper proposes a novel evolutionary algorithm that blends the exploitative and explorative merits of two main evolutionary algorithms, namely the Stochastic Fractal Search (SFS) and a Differential Evolution (DE) variant. This amalgam has an effective interaction and cooperation of an ensemble of diverse strategies to derive a single framework called Iterative Cyclic Tri-strategy with adaptive Differential Stochastic Fractal Evolutionary Algorithm (Ic3-aDSF-EA). The component algorithms cooperate and compete to enhance the quality of the generated solutions and complement each other. The iterative cycles in the proposed algorithm consist of three consecutive phases. The main idea behind the cyclic nature of Ic3-aDSF-EA is to gradually emphasize the work of the best-performing algorithm without ignoring the effects of the other inferior algorithm during the search process. The cooperation of component algorithms takes place at the end of each cycle for information sharing and the quality of solutions for the next cycle. The algorithm's performance is evaluated on 43 problems from three different benchmark suites. The paper also investigates the application to a set of real-life problems. The overall results show that the proposed Ic3-aDSF-EA has a propitious performance and a reliable scalability behavior compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Many real-world problems can be formulated as optimization problems. Such problems pose a challenge for researchers in the design of efficient algorithms capable of finding the best solution with the least computational cost. In this paper, a new evolutionary algorithm is proposed that combines the explorative and exploitative capabilities of two evolutionary algorithms, Cultural Algorithm (CA) and Differential Evolution (DE) algorithm. This hybridization follows the HTH (High-level Teamwork Hybrid) nomenclature in which two meta-heuristics are executed in parallel. The new algorithm named as CADE, manages an overall population which is shared between CA and DE simultaneously. Four modified knowledge sources have been used in proposed CA which are: topographical, situational, normative and domain. The role of the used acceptance function in belief space is to select the knowledge of the best individuals to update the current knowledge. A novel quality function is used to determine the participation ratio for both CA and DE, and then a competitive selection takes place in order to select the proportion of function evaluations allocated for each technique. This collaborative synergy emerges between the DE and CA techniques and is shown to improve the quality of solutions, beyond what each of these two algorithms alone. The performance of the algorithm is evaluated on a set of 50 scalable optimization problems taken from two sources. The first set of 35 came from existing benchmark sets available in the literature. The second set came from the 2014 IEEE Single Function optimization competition. The overall results show that CADE has a favorable performance and scalability behaviors when compared to other recent state-of-the-art algorithms. CADE's overall performance ranked at number 1 for each of the two sets of problems. It is suggested that CADE's success across such a broad spectrum of problem types and complexities bodes well for its application to new and novel applications. [ABSTRACT FROM AUTHOR]

The aim of hybridization in the context of evolutionary computation is to combine appropriate operators from different evolutionary computation paradigms to form a single technique that enjoys a statistically superior performance over a wide range of optimization problems. This paper introduces a novel hybridization between differential evolution and update processes of the stochastic fractal search algorithm. The diffusion property of the fractal search algorithm is applied in random fractals followed by two novel update processes to explore the search space more efficiently. In this algorithm, a diffusion process based on differential evolution algorithm is used instead of random fractals in the original stochastic fractal search algorithm. A new success-based scheme is used to utilize the update processes and to solve the burden of extra computations during the search. This new algorithm captures the strengths of both component algorithms and produces a greater explorative power as compared to the original algorithms. To verify the performance of our algorithm, a challenging test suite of 30 benchmark functions from the IEEE CEC2014 real parameter single objective competition is used. The results affirm the effectiveness and robustness of the proposed approach compared to the original stochastic fractal search and other recent state-of-the-art algorithms. The proposed algorithm enjoys a statistically superior performance over most of the tested benchmarks, especially hybrid and composition test functions compared to the other contestant algorithms. [ABSTRACT FROM AUTHOR]

In recent years, Cultural Algorithms (CAs) have attracted substantial research interest. When applied to highly multimodal and high dimensional problems, Cultural Algorithms suffer from fast convergence followed by stagnation. This research proposes a novel hybridization between Cultural Algorithms and a modified multiple trajectory search (MTS). In this hybridization, a modified version of Cultural Algorithms is applied to generate solutions using three knowledge sources namely situational knowledge, normative knowledge, and topographic knowledge. From these solutions, several are selected to be used by the modified multi-trajectory search. All solutions generated by both component algorithms are used to update the three knowledge sources in the belief space of Cultural Algorithms. In addition, an adaptive quality function is used to control the number of function evaluations assigned to each component algorithm according to their success rates in the recent past iterations. The function evaluations assigned to Cultural Algorithms are also divided among the three knowledge sources according to their success rates in recent generations of the search. Moreover, the quality function is used to tune the number of offspring these component algorithms are allowed to contribute during the search. The proposed hybridization between Cultural Algorithms and the modified trajectory-based search is employed to solve a test suite of 25 large-scale benchmark functions. The paper also investigates the application of the new algorithm to a set of real-life problems. Comparative studies show that the proposed algorithm can have superior performance on more complex higher dimensional multimodal optimization problems when compared with several other hybrid and single population optimizers. [ABSTRACT FROM AUTHOR]

Hybridization in context to Evolutionary Computation (EC) strives to combine operators, components, and the best merits of different EC paradigms, to form a new evolutionary algorithm that enjoys a statistically superior performance, compared to its ancestors, over a wide range of application-specific optimization problems. In this paper, we propose a simple yet powerful amalgam composed of a modified Cultural Algorithm (CA) that is supported with an Enhanced Levy Flight Search (ELFS) to guide the search and further promote the harmony between the explorative and exploitative capacities of the conventional techniques. The novel amalgam, denoted by q-a CA + m IS, utilizes a balanced search scheme where it employs an adapted Influence Function (IF) with a novel quality function that establishes a harmony between the Knowledge Sources (KSs) in the Belief Space (BS), and between the BS and other components in the hybrid to produce the most suitable knowledge needed for a certain search mode. The CA framework is reinforced with an updated Selection Function (SF) that employs a successful selection strategy that uses the extended situational knowledge for the future selection of individuals. The proposed algorithm is tested using more than 50 benchmark functions that are taken from the IEEE CEC'06, and the IEEE CEC'19 competitions on constrained real-parameter optimization. Moreover, three well-known engineering design problems are used to test the validity of the algorithm for the solution of complex real-life problems. The comparative study indicates that the q-a CA + m IS algorithm was able to obtain a statistically superior performance and scalability behavior over most of the considered functions in comparison with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Contemporary real-world optimization benchmarks are subject to many constraints and are often high-dimensional problems. Typically, such problems are expensive in terms of computational time and cost. Conventional constraint-based solvers that are used to tackle such problems require a considerable high budget of function evaluations. Such budget is not affordable in practice. In most cases, this number is considered the termination criterion in which the optimization process is stopped and then the best solution is marked. The algorithm might not converge even after consuming the pre-defined number of function evaluations, and hence it does not guarantee an optimal solution is found. Motivated by this consideration, this paper introduces an effective surrogate model to assist the differential evolution algorithm to generate competitive solutions during the search process. The proposed surrogate model uses a new adaptation scheme to adapt the theta parameter in the well-known Kriging model. This variable determines the correlation between the parameters of the optimization problem being solved. For that reason, an accurate surrogate model is crucial to have a noticeable enhancement during the search. The statistical information exploited from a covariance matrix is used to build the correlation matrix to adapt the theta variable instead of using a fixed value during the search. Hence, the surrogate model evolves over the generations to better model the basin of the search, as the population evolves. The model is implemented in the popular L-SHADE algorithm. Two benchmark sets: bound-constrained problems and real-world optimization problems are used to validate the performance of the proposed algorithm, namely iDE a Sm. Also, two engineering design problems are solved: welded beam and pressure vessel. The performance of the proposed work is compared with other state-of-the-art algorithms and the simulation results indicate that the new technique can improve the performance to generate better statistical significance solutions. [ABSTRACT FROM AUTHOR]