Quasi-optimal Solutions Research Articles

Intelligent Design of Street Lamp in Rural Areas Based on an Improved Genetic Algorithm

This study addresses the demand for more efficient streetlight designs in rural areas by introducing an improved genetic algorithm (GA) to optimize the geometry and placement of streetlight poles. Conventional GAs frequently suffer from premature convergence and becoming trapped in local optima, reducing their effectiveness. To mitigate these issues, this research integrates the genetic algorithm with Sequential Quadratic Programming (SQP), using the quasi-optimal solution generated by the GA as the initial input for the SQP, enhancing both accuracy and stability. The methodology includes developing a geometric model of streetlight poles utilizing point cloud data and extracting the centerline via the optimized GA-SQP approach. Additionally, the study examines the effects of random errors, gross errors, incomplete point cloud data, and centerline deviations on the algorithm's performance.

A Fast Quantum Algorithm for Searching the Quasi-Optimal Solutions of Unit Commitment

A Fast Quantum Algorithm for Searching the Quasi-Optimal Solutions of Unit Commitment

МОДЕЛЮВАННЯ ХАРАКТЕРИСТИК ПРОЦЕСІВ У БІОГАЗОВИХ УСТАНОВКАХ НА ОСНОВІ АНАЛІЗУ ІНТЕРВАЛЬНИХ ДАНИХ

The problems of the research presented in the work relate to mathematical modeling in order to reflect the relationship between the main characteristic of the process and the factors that affect it, as well as the dynamics of the main characteristic of the process, which is determined by the acidity of the substrate in the bioreactor. To build mathematical models of both types, it is proposed to use the methods of parametric and structural identification of models of static objects and discrete models of object dynamics based on the analysis of interval data. An universal approach based on metaheuristic optimization algorithms is proposed and substantiated for the identification of both types of models. These methods, in turn, use mechanisms of self-organization and self-adaptation in the process of finding an optimal or quasi-optimal solution. In particular, the work uses computational algorithms built on the basis of artificial bee colony algorithms. The method is implemented using data presented in interval form. The proposed universal method was tested on the construction of a mathematical model that reflects the dependence between the pH of the fermentation medium and the volume of the loaded bio-raw material in the form of its dry and liquid parts, the temperature in the bioreactor and the humidity of the dry part of the bio-raw material. Another mathematical model built in the work reflects the dynamics of the acidity indicator pH of the fermentation medium depending on the ratio of the mass of the loaded dry bio-raw material to the volume of the loaded liquid bio-raw material Both obtained interval mathematical models can be applied to control processes in biogas plants.

OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems.

This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while preventing the algorithm from relying on specific members of the population. We use a one-to-one correspondence between the two sets of population members and the members selected as guides to increase the involvement of all population members in the update process. Each population member is chosen just once as a guide and is only utilized to update another member of the population in this one-to-one interaction. The proposed OOBO's performance in optimization is evaluated with fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results highlight the remarkable capacity of OOBO to strike a balance between exploration and exploitation within the problem-solving space during the search process. The quality of the optimization results achieved using the proposed OOBO is evaluated by comparing them to eight well-known algorithms. The simulation findings show that OOBO outperforms the other algorithms in addressing optimization problems and can give more acceptable quasi-optimal solutions. Also, the implementation of OOBO in six engineering problems shows the effectiveness of the proposed approach in solving real-world optimization applications.

Modified Bioinspired Method for Decision-Making Support for Prevention and Elimination of the Emergencie's Consequences

The work is devoted to solving the scientific problem of decision support for the prevention and elimination of emergency situations (ES) consequences based on fuzzy logic and machine learning methods. The relevance of this problem is due to the need to optimize the risk of adverse effects on human health and the environment in connection with emergency situations. The increased complexity of the tasks solved within the framework of the designated scientific problem is associated with the presence of information uncertainty in the complex accounting of heterogeneous characteristics, which in some cases can’t be normalized and brought to a single measurement scale. In such conditions, information processes for predicting the occurrence of potentially dangerous chains of events in the technogenic and natural spheres must be built using artificial intelligence methods and fuzzy logic to increase the efficiency of choosing the sequence of actions performed on the available information in order to build the necessary models, methods and algorithms to eliminate negative development situations and ensure monitoring of emergencies potential development cases. The authors give formalized statements of the tasks to be solved. A conceptual data model is proposed for constructing fuzzy decision support rules for the prevention and elimination of emergencies’ consequences. One of the options for formalizing such a data model is the transition to a vector representation of the information space. This will allow in the future to solve the problem of their classification on a set of information elements for distribution by classes of emergency situations. The criterion for evaluating belonging to a certain class is the argument for minimizing the distance between information elements in the vector space. The procedure for the accumulation by an intelligent system of precedent models set for the prevention or elimination of the emergencies consequences, which is a stage of machine learning, is described. After passing it, the intelligent system becomes capable of assessing the semantic similarity of operationally obtained models with precedents that have already fallen into the category of templates. The criterion for evaluating the effectiveness of an intelligent decision support system is the semantic similarity of the operational situational emergency model and the precedent model. A heuristic algorithm for determining semantic proximity was proposed. In order to optimize the time spent on supporting decision-making on the prevention and elimination of the emergency situations consequences, the authors also propose to use decentralized bioinspired methods, the advantages of which are internal procedures that provide diversification of the search space to exit from local optima and quickly obtain quasi-optimal solutions to the problem. The development of a modified bacterial optimization method (MMBO) was described. A software application has been created to conduct a computational experiment. The results of the conducted studies confirmed the advantages of the bacterial optimization proposed modified method.

Four-order superconvergent CDG finite elements for the biharmonic equation on triangular meshes

In a conforming discontinuous Galerkin (CDG) finite element method, discontinuous Pk polynomials are employed. To connect discontinuous functions, the inter-element traces, {uh} and {∇uh}, are usually defined as some averages in discontinuous Galerkin finite element methods. But in this CDG finite element method, they are defined as projections of a lifted Pk+4 polynomial from four Pk polynomials on neighboring triangles. With properly chosen weak Hessian spaces, when tested by discontinuous polynomials, the variation form can have no inter-element integral, neither any stabilizer. That is, the bilinear form is the same as that of conforming finite elements for solving the biharmonic equation. Such a conforming discontinuous Galerkin finite element method converges four orders above the optimal order, i.e., the Pk solution has an O(hk+5) convergence in L2-norm, and an O(hk+3) convergence in H2-norm. A local post-process is defined, which lifts the Pk solution to a Pk+4 quasi-optimal solution. Numerical tests are provided, confirming the theory.

Optimizing a Multi-Layer Perceptron Based on an Improved Gray Wolf Algorithm to Identify Plant Diseases

Metaheuristic optimization algorithms play a crucial role in optimization problems. However, the traditional identification methods have the following problems: (1) difficulties in nonlinear data processing; (2) high error rates caused by local stagnation; and (3) low classification rates resulting from premature convergence. This paper proposed a variant based on the gray wolf optimization algorithm (GWO) with chaotic disturbance, candidate migration, and attacking mechanisms, naming it the enhanced gray wolf optimizer (EGWO), to solve the problem of premature convergence and local stagnation. The performance of the EGWO was tested on IEEE CEC 2014 benchmark functions, and the results of the EGWO were compared with the performance of three GWO variants, five traditional and popular algorithms, and six recent algorithms. In addition, EGWO optimized the weights and biases of a multi-layer perceptron (MLP) and proposed an EGWO-MLP disease identification model; the model was tested on IEEE CEC 2014 benchmark functions, and EGWO-MLP was verified by UCI dataset including Tic-Tac-Toe, Heart, XOR, and Balloon datasets. The experimental results demonstrate that the proposed EGWO-MLP model can effectively avoid local optimization problems and premature convergence and provide a quasi-optimal solution for the optimization problem.

An efficient evolutionary algorithm for high-speed train rescheduling under a partial station blockage

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. Firstly, a HSTR model subject to train operation constraints is established to minimize the total train delay. A permutation-based encoding method is developed to define an efficient search space based on the train departure sequence. A heuristic decoding method is employed to eliminate all train operation constraints and output the rescheduled timetable. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of PS-SEGA. Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced evolutionary algorithms and their improved variants also using the improvements of PS-SEGA, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX cannot obtain optimal solutions within 10 min, PS-SEGA can provide quasi-optimal solutions in real time. Furthermore, compared with the other two heuristics algorithms (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can give the train departure sequence with a smaller total train delay.

Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering

Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions to optimization problems. The main inspiration for the DA is to simulate the selection of objects from different drawers to create an optimal combination. The optimization process involves a dresser with a given number of drawers, where similar items are placed in each drawer. The optimization is based on selecting suitable items, discarding unsuitable ones from different drawers, and assembling them into an appropriate combination. The DA is described, and its mathematical modeling is presented. The performance of the DA in optimization is tested by solving fifty-two objective functions of various unimodal and multimodal types and the CEC 2017 test suite. The results of the DA are compared to the performance of twelve well-known algorithms. The simulation results demonstrate that the DA, with a proper balance between exploration and exploitation, produces suitable solutions. Furthermore, comparing the performance of optimization algorithms shows that the DA is an effective approach for solving optimization problems and is much more competitive than the twelve algorithms against which it was compared to. Additionally, the implementation of the DA on twenty-two constrained problems from the CEC 2011 test suite demonstrates its high efficiency in handling optimization problems in real-world applications.

A grounded inerter-based oscillating TMD for suppressing harmonic and random vibrations

Inspired by the lightweight passive vibration control technology, in this work a new anti-vibration device is proposed to significatively improve the protection for stationary mechanical structures subjected to different kinds of vibratory loads. Such a device is herein termed as the grounded inerter-based two-degree-of-freedom tuned mass damper (GI-TDOF-TMD) composed by a grounded inerter and TDOF-TMD with rotational and translational motions, which controls the primary structure's dominant modal shape by using the synergistic dynamic effects of both devices. In order to fairly evaluate the GI-TDOF-TMD's vibration mitigation effectiveness with respect to other devices, the damped and undamped primary structure's nondimensional compliance and mobility transfer functions are firstly computed from a generalized mathematical model that considers four types of random and harmonic excitation inputs which are the following: force general case, base acceleration excitation, base displacement input and inertial force generated by the unbalance in rotary machinery. For harmonically excited mechanical systems, the H∞ performance measure is applied to minimize the maximum resonant peaks of the displacement and velocity dimensionless frequency response functions (FRFs). As the Extended Fixed-Points Technique (EFPT) is analogous to the H∞ criterion, quasi-optimal solutions are firstly computed to perfectly calibrate the FRF's invariant points. After performing this, the evolution of the FRF's control invariant frequencies revealed that GI-TDOF-TMD performs well at low inertance values. Therefore, the GI-TDOF-TMD's maximum control performance is obtained when the inerter's inertial force is the same as that yielded by the TMD's physical mass. In view of such a dynamic behavior, the GI-TDOF-TMD approximately provides 40% and 17% improvements when compared with respect to the classic dynamic vibration absorber (DVA) and TMD-inerter (TMDI), respectively. Then, the GI-TDOF-TMD's power dissipation capability is demonstrated by applying the H2 norm approach to the randomly excited damped mechanical structures. Additionally, it is also revealed through the stochastic energy balance that the GI-TDOF-TMD's internal power dissipation effectiveness is directly reflected on the minimization of the primary structure's kinetic energy. Moreover, for random inputs, the proposed device can broaden the effective operating bandwidth in approximately 45% and 19% when compared to the classic DVA and TMDI, respectively. Therefore, the GI-TDOF-TMD works better than the TMDI in terms of structural displacement and velocity response mitigation, which can be useful in civil engineering applications.

Quasioptimal Spacecraft Attitude Control Constructed According to the Poinsot Concept

The problem of attitude optimal control of a spacecraft as a solid body with the energy spent on maneuvering a spacecraft taken as a quadratic functional, and a fixed transition time, is investigated. The dynamic configuration of a spacecraft and boundary conditions on an attitude and angular velocity are arbitrary; the control function is not limited. According to the Poinsot concept from theoretical mechanics on the interpretation of the angular motion of a solid body around a fixed point and with the help of the Pontryagin maximum principle, the quasioptimal analytical solution of the problem is obtained, which is brought to the algorithm. Numerical examples are provided that confirm the closeness of the quasioptimal solution to the optimal solution of the problem.

Two-step approach for quasi-optimization of energy storage and transportation at renewable energy site

In this paper, the authors present a two-step model to obtain quasi-optimal solutions for the storage and transportation of renewable energy as its installation becomes more widespread in remote areas. The model is formulated as a total cost minimization linear programming problem and considers both renewable energy facilities and the energy supply and demand of the entire country. The first step of the model optimizes the entire country, while the second step determines the marginal capacity of energy storage and transportation facilities when the excess production capacity of remote renewable energy is added. This approximation for reducing computational complexity is shown to be sufficiently accurate under realistic conditions. The authors apply the model to analyze energy storage and hydrogen production at renewable energy power plants in Japan in 2050. The study shows that photovoltaic power generation uses storage batteries attached to the power plant for electricity transmission. In contrast, wind power generation transmits most electricity directly without storage batteries and converts some into hydrogen for consumers. The proposed lightweight model allows for numeral sensitivity analyses, which provide valuable insight into optimizing energy storage and transport of renewable energy.

The hybrid average subtraction and standard deviation based optimizer

In this paper, we propose a new metaheuristic algorithm named the Hybrid Average Subtraction and Standard Deviation based Optimizer (HASSO) to solve the optimization problem. The proposed algorithm uses the amount of information gathered from averages, subtraction, standard deviation, and hybrid population members to explore the search space in order to reach the near-optimal or quasi-optimal solution. The mathematical modeling of the proposed algorithm has been presented in detail. We solve twenty-three well-known important functions containing the unimodal and multimodal functions to obtain their quasi-optimal solution. The results obtained for unimodal and multimodal functions indicate the high exploitation ability of HASSO in converging towards the global optima. The convergence plot of the algorithm shows that the use of five phases in the algorithm makes the algorithm tend towards the optimal or quasi-optimal solution in fewer iterations. In addition to this, the obtained results are compared with nine classical algorithms, and the sensitivity of the algorithm to the population size and number of iterations is also evaluated. Our algorithm is also statistically analyzed for superiority over other algorithms. From this analysis, HASSO’s superiority over the other nine classical algorithms in providing a highly accurate solution with a lesser number of iterations is clearly shown.

GA-aided directivity in volumetric and planar massive-antenna array design

The problem of directivity enhancement, leading to the increasing in the directivity gain over a certain desired angle of arrive/departure (AoA/AoD), is considered in this work. A new formulation on the volumetric array directivity problem is proposed using the rectangular coordinates to describe each antenna element, and the desired azimuth and elevation angles with a general element pattern. Such directivity problem is formulated to find the optimal minimum distance between the antenna-elements dmin aiming to achieve as high directivity gains as possible. An expedite implementation method is developed consisting in placing the antenna-elements in a distinctive plane dependent on (θ0; ϕ0). A novel concept on the optimizing directivity for the uniform planar array (OUPA) is introduced to find a quasi-optimal solution for the non-convex optimization problem with low-complexity. This solution is reached deploying the proposed successive evaluation and validation (SEV) method. Moreover, the genetic algorithm (GA) method was deployed to expeditiously find the directivity optimization solution. For a small number of antenna-elements, typically N∈[4,⋯,9], the achievable directivity by GA optimization demonstrates gains of ∼3 dBi compared with the regular beamforming technique, using steering vector for uniform linear arrays (ULA) and uniform circular arrays (UCA), while gains of ∼1.5 dBi is attained when compared with an improved UCA directivity method. For a larger number of antenna-elements, two improved GA procedures, namely GA-marginal and GA-stall, were proposed and compared with OUPA method. OUPA also indicates a promising directivity gains surpassing 30 dBi for massive MIMO scenarios.

Real-time rescheduling approach of train operation for high-speed railways using problem-specific knowledge under a station blockage

This study considers the train timetable as the problem object to investigate the station blockage caused by emergencies. Based on the evolutionary algorithm framework, a real-time rescheduling approach using problem-specific knowledge is proposed. This approach ensures the safety and efficiency of high-speed rail operations and the satisfaction and comfort of passengers by minimizing the total train delay. First, a permutation encoding method is developed to reduce invalid searches in the search space based on the rescheduling strategy of reordering train departure sequences. Next, according to the train operation tracking mode, called tracking interval control, a heuristic decoding method is designed to eliminate all constraints of train operation to improve efficiency. Finally, the dispatcher's experience in adjusting the train timetable is used as the problem-specific knowledge to initialize the initial population of evolutionary computing. A heuristic population initialization method based on problem-specific knowledge is employed to speed up the algorithm convergence in the early stage and improve solution equality. Furthermore, the Beijing–Tianjin high-speed railway line is used as an example. Nine typical scenarios with different blockage durations of 20–150 min under the station blockage are installed at the Beijing South station. The strengthened elitist genetic algorithm (SEGA) and differential evolution (DE) are selected to perform the simulation using different combinations of the real-integer or permutation encoding and random or heuristic population initialization, respectively. The simulation results indicate that, compared with the real-integer encoding that cannot obtain feasible solutions, the two evolutionary algorithms can provide the rescheduling solution with a smaller total train delay in an average time of 9 s after using the permutation encoding. Besides, the results of the two evolutionary algorithms improved by the heuristic population initialization can quickly converge to a quasi-optimal solution. Finally, the SEGA in the permutation encoding with the heuristic population initialization is chosen as the optimal improved evolutionary algorithm. This improved evolutionary algorithm can provide quasi-optimal solutions in 20 s in the seven scenarios where the CPLEX cannot provide optimal solutions in 10 min.

Computational Performance Evaluation of Column Generation and Generate-and-Solve Techniques for the One-Dimensional Cutting Stock Problem

The Cutting Stock Problem (CSP) is an optimisation problem that roughly consists of cutting large objects in order to produce small items. The computational effort for solving this problem is largely affected by the number of cutting patterns. In this article, in order to cope with large instances of the One-Dimensional Cutting Stock Problem (1D-CSP), we resort to a pattern generating procedure and propose a strategy to restrict the number of patterns generated. Integer Linear Programming (ILP) models, an implementation of the Column Generation (CG) technique, and an application of the Generate-and-Solve (G&S) framework were used to obtain solutions for benchmark instances from the literature. The exact method was capable of solving small and medium sized instances of the problem. For large sized instances, the exact method was not applicable, while the effectiveness of the other methods depended on the characteristics of the instances. In general, the G&S method presented successful results, obtaining quasi-optimal solutions for the majority of the instances, by employing the strategy of artificially reducing the number of cutting patterns and by exploiting them in a heuristic framework.

Hybrid Annealing Method Based on subQUBO Model Extraction With Multiple Solution Instances

Ising machines are expected to solve combinatorial optimization problems efficiently by representing them as Ising models or equivalent quadratic unconstrained binary optimization (QUBO) models . However, upper bound exists on the computable problem size due to the hardware limitations of Ising machines. This paper propose a new hybrid annealing method based on partial QUBO extraction, called subQUBO model extraction, with multiple solution instances. For a given QUBO model, the proposed method obtains <inline-formula><tex-math notation="LaTeX">$N_I$</tex-math></inline-formula> quasi-optimal solutions (quasi-ground-state solutions) in some way using a classical computer. The solutions giving these quasi-optimal solutions are called <i>solution instances</i> . We extract a size-limited subQUBO model as follows based on a strong theoretical background: we randomly select <inline-formula><tex-math notation="LaTeX">$N_S$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$(N_S&lt;N_I)$</tex-math></inline-formula> solution instances among them and focus on a particular binary variable <inline-formula><tex-math notation="LaTeX">$x_i$</tex-math></inline-formula> in the <inline-formula><tex-math notation="LaTeX">$N_S$</tex-math></inline-formula> solution instances. If <inline-formula><tex-math notation="LaTeX">$x_i$</tex-math></inline-formula> value is much <i>varied</i> over <inline-formula><tex-math notation="LaTeX">$N_S$</tex-math></inline-formula> solution instances, it is included in the subQUBO model; otherwise, it is not. We find a (quasi-)ground-state solution of the extracted subQUBO model using an Ising machine and add it as a new solution instance. By repeating this process, we can finally obtain a (quasi-)ground-state solution of the original QUBO model. Experimental evaluations confirm that the proposed method can obtain better quasi-ground-state solution than existing methods for large-sized QUBO models.

Optimisation of flow shop scheduling problem: simulation system vs. evolutive solver

In the Industry 4.0 era the optimisation of production processes become more and more important. A wide range of tools are used to improve the performance of production processes and related operations of the supply chain including purchasing, distribution and inverse processes. These tools have a great impact on the results of design and operation of production processes both technology and logistics point of view. Within the frame of this article, the authors are demonstrating the optimisation potentials of simulation tools and heuristic solvers (optimisers) through the problem of the well-knows permutation flow shop scheduling. They are describing the models and methods used in the case of FlexSim simulation systems and the heuristic solver option of Excel Solver. As the numerical results show, both the simulation model and the heuristic optimisation approach lead to a quasi-optimal solution.

Heuristic procedure for synthesis of separation system for multicomponent mixtures using fuzzy logic

In this study, the task is to develop a heuristic procedure for the synthesis of systems for the separation of multicomponent mixtures, which would take into account the uncertainty of the factors of the separation processes and the multivariance of the solution.&#x0D; The authors reviewed the current state of the existing methods for the separation of mixtures. Many works devoted to the synthesis of effective systems for the separation of multicomponent mixtures leave unsolved the problem of taking into account the uncertainty of many factors of the separation processes. In the presented work, it is proposed that a heuristic approach is applied using fuzzy logic.&#x0D; The proposed procedure is based on the choice of a separation sequence that takes into account the value of the separation coefficient and the difference in boiling points of two adjacent key components. The proposed strategy is based on an estimate of the likelihood of each rule for each possible separation. The search for solutions has five stages. First, the mixture to be separated and the available equipment are determined. After that, an ordered list of all separation options is drawn up with possible values of the economic costs of their implementation. Next, the mass load of possible separation is estimated and the separation with the minimum value of the separation ratio is selected. The heuristic is that the heavy separation is done last. And, at the last step, the most economical separation scheme is selected.&#x0D; The above strategy at the stage of quantitative assessment is subject to the value of the threshold S (boiling point difference). This point can be used to determine the quasi-optimal solutions to the problem of sequencing according to the rules of relaxation of separation. &#x0D; For this fuzzy heuristic method for determining cost-effective separation sequences for mixtures, appropriate software has been developed. In addition, since the procedure uses the theory of fuzzy sets, the Fuzzy Logic Toolbox package of the Matlab was also used to solve the problem of synthesizing schemes for separating hydrocarbon mixtures. The proposed procedure was successfully tested in both software implementations using the example of separating a five-component mixture. &#x0D; The main difference between the proposed approach and other approaches based on fuzzy logic for the synthesis of separation systems is the gradation of working rules and possible quantification of a set of rules, which eliminates conflicts between the rules.

A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems.

In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO’s performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO’s exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.