Multi-objective Beetle Swarmoptimization for Portfolio Selection

Beetle Antennae Search (BAS) is an intelligent algorithm that mimics the beetle preying process. In BAS, there is a beetle which has a left and a right antennae sensing the intensity of the odor emitted by the food. The beetle learns the different sense of two antennas to determine the flight and finally finds the food. The BAS algorithm has been proved to have good optimizing speed and precision when it is applied for low-dimensional optimization problems. However, when solving high-dimensional problems, the algorithm is easily trapped into local optimum. In order to improve the optimization ability of BAS, we have combined particle swarm optimization (PSO) algorithm and proposed a new hybrid BAS and PSO algorithm (BAS-PSO). In BAS-PSO, firstly, the standard PSO is used for particle velocity and position updating. Particles learn ontology information and group optimal information for evolution. Then each particle in the swarm is taken as a beetle independently applying a local search with BAS algorithm. To verify the performance of BAS-PSO, four benchmark functions have been selected in our simulation experiments. The results show that the performance of BAS-PSO is better than BAS and PSO.

Dual Beetle Antennae Search system for optimal planning and robust control of 5-link biped robots

<title>Abstract</title> Image enhancement is a crucial process in various fields, including medical imaging, remote sensing, and digital photography. This study explores the application of the Beetle Antennae Search (BAS) algorithm for optimizing image enhancement parameters. The BAS algorithm is compared with the Particle Swarm Optimization (PSO) algorithm to evaluate its effectiveness in improving image quality. The objective is to enhance grayscale images by adjusting parameters such as A, B, C, and K to maximize a fitness function that incorporates edge information and entropy. Experimental results demonstrate that BAS consistently outperforms PSO in terms of convergence speed and image quality metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). BAS-enhanced images exhibit significant improvements in contrast, brightness, and sharpness, making it a promising tool for image enhancement tasks.

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems. This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.

In order to solve the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and fairness of users in cognitive radio network, based on quantum bee colony theory and membrane computing, a novel multi-objective discrete combinatorial optimization algorithm, named membrane-inspired quantum bee optimization, is proposed. The global optimal solution of single objective can be searched in the elementary membranes, and Pareto front solutions which take account of network utility and fairness, can be obtained from skin membrane with the proposed method. The multi-objective optimization algorithm, which can solve both single objective and multi-objective optimization problems at the same time, is designed by the communication rules between membranes, the cooperative evolution of foraging behavior based on quantum state, and non-dominated sorting. Compared with classical color-sensitive graph coloring algorithm, genetic algorithm, quantum genetic algorithm, and particle swarm optimization under different objective functions, the proposed spectrum allocation method can search the global optimal solution of single objective as shown by the simulation results, and it is superior to classical spectrum allocation algorithms and existing intelligence spectrum allocation methods. The optimal Pareto front solutions of multi-objective spectrum allocation are also obtained.

Particle swarm optimization (PSO) and differential evolution (DE) are meta-heuristics which have been found to be successful in a wide variety of optimization tasks. The high speed of convergence and the relative simplicity of PSO make it a highly viable candidate to be used in multi-objective optimization problems (MOPs). Therefore, several PSO approaches capable to handle MOPs (MOPSOs) have appeared in the past. There are some problems, however, where PSO-based algorithms have shown a premature convergence. On the other hand, multi- objective DEs (MODE) have shown lower speed of convergence than MOPSOs but they have been successfully used in problems where MOPSO have mistakenly converged. In this work, we have developed experiments to observe the convergence behavior, the online convergence, and the diversity of solutions of both meta-heuristics in order to have a better understanding about how particles and solutions move in the search space. To this end, MOPSO and MODE algorithms under (to our best effort) similar conditions were used. Moreover, the ZDT test suite was used on all experiments since it allows to observe Pareto fronts in two-dimensional scatter plots (more details on this are presented on the experiments section). Based on the observations found, modifications to two PSO-based algorithms from the state of the art were proposed resulting in a rise on their performance. It is concluded that MOPSO presents a poor distributed scheme that leads to a more aggressive search. This aggressiveness showed to be detrimental for the selected problems. On the other hand, MODE seemed to generate better distributed points on both decision and objective space allowing it to produce better results.

The allocation of limited capital to a variety of assets available is one of the important problems in financial management. It is actually a constrained multi-objective optimization problem called portfolio selection. Most studies have been made to solve the problem with single-objective optimization techniques by aggregating conflicting and incommensurate objectives into a single one. The solutions obtained may be unsatisfactory because their nondominance is not guaranteed, especially when some practical constraints, such as cardinality constraints, are incorporated into the portfolio optimization models. This paper presents an evolutionary multi-objective optimization technique, which called Knapsack Multi-Objective Particle Swarm Optimization (K-MOPSO) to generate the efficient portfolios in terms of expected return and variance under short sales and/or cardinality constraints. Finally, a Multi-Attribute Decision Making (MADM) method named Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to outrank the efficient portfolio that decision makers satisfy most.

In a real life situation, due to the complexity of the industrial systems and non-linearity of their behavior, it is very difficult to achieve optimum performance of system for desired industrial goals using uncertain, vague and imprecise data. Herein, an approach has been proposed through which the behavior of the system is analyzed in the form of well- known six reliability indices by using triangular fuzzy numbers which allow the consideration of expert opinions, linguistic variables, operating conditions in reliability information. Using their behavior analysis a fuzzy multi-objective optimization problem (FMOOP) has been formulated. Due to the conflicting nature of the multiple objectives, the decision making is difficult and it leads to the Pareto optimal solutions instead of single optimal solutions. Many evolutionary algorithms (EAs) already exist in the literature for solving a multi-objective optimization problem (MOOP), and are termed as multi-objective evolutionary algorithms (MOEAs). Particle swarm optimization (PSO) is one of such MOEA which demonstrates the ability to identity a Pareto-optimal front efficiently. Here, a crisp optimization problem is reformulated from FMOOP by taking into account the preference of decision maker (DM) and then PSO is applied to solve the resulting fuzzified MOOP. The presented approach is applied in order to solve the multi-objective series-parallel system reliability optimization problem for a crystallization unit of a urea plant.

In this paper, we focuses on the theoretical analysis on the convergence of the beetle antennae search (BAS) algorithm. For the first time, we prove that the BAS algorithm is convergent with probability 1, which provides some insights about why the BAS algorithm works and also brings us some ideas about how to improve the BAS algorithm in the future. We also test the performance of the BAS algorithm via some representative benchmark functions. Meanwhile, some applications of the BAS algorithm are also presented, showing the advantages of the algorithm over other algorithms.

Ship predictive collision avoidance method based on an improved beetle antennae search algorithm

Ultra-high-performance concrete (UHPC) is a recently developed material which has attracted considerable attention in the field of civil engineering because of its outstanding characteristics. One of the key factors in concrete design is the compressive strength (CS) of UHPC. As one of the most potent tools in artificial intelligence (AI), machine learning (ML) can accurately predict concrete’s mechanical properties. Hyperparameter tuning is crucial in ensuring the prediction model’s reliability. However, it is a complex work. The purpose of this study is to optimize the CS prediction method for UHPC. Three ML methods, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN), are selected to predict the CS of UHPC. Among them, the RF model demonstrates superior predictive accuracy, with the testing dataset R2 of 0.8506. In addition, three meta-heuristic optimization algorithms, particle swarm optimization (PSO), beetle antenna search (BAS), and snake optimization (SO), are utilized to optimize the prediction model hyperparameters. The R2 values for the testing dataset of SO-RF, PSO-RF, and BAS-RF are 0.9147, 0.8529, and 0.8607, respectively. The results indicate that SO-RF exhibits the highest predictive performance. Furthermore, the importance of input parameters is evaluated, and the findings prove the feasibility of the SO-RF model. This research enriches the prediction method of the CS of UHPC.

Our purpose in this paper is to depart from the intrinsic pathology of the typical mean–variance formalism, due to both the restriction of its assumptions and difficulty of implementation. We manage to co-assess a set of sophisticated real-world non-convex investment policy limitations, such as cardinality constraints, buy-in thresholds, transaction costs, particular normative rules, etc., within the frame of complex scenarios, which demand for simultaneous optimization of multiple investment objectives. In such a case, the portfolio selection process reflects a mixed-integer multiobjective portfolio optimization problem. On this basis, we meticulously develop all the corresponding modeling procedures and then solve the underlying problem by use of a new, fast and very effective algorithm. The value of the suggested framework is integrated with the introduction of two novel concepts in the field of multiobjective portfolio optimization, i.e. the security impact plane and the barycentric portfolio. The first represents a measure of each security's impact in the efficient surface of Pareto optimal portfolios. The second serves as the vehicle for implementing a balanced strategy of iterative portfolio tuning. Moreover, a couple of some very informative graphs provide thorough visualization of all empirical testing results. The validity of the attempt is verified through an illustrative application on the Eurostoxx 50. The results obtained are characterized as very encouraging, since a sufficient number of efficient or Pareto optimal portfolios produced by the model, appear to possess superior out-of-sample returns with respect to the underlying benchmark.

The aim of the work is to create a scenario modeling technique based on adaptation of metaheuristic multi-objective optimization algorithms to construct Pareto-optimal investment portfolios taking into account multiple restrictions on the company's portfolio. Investment portfolio is a tool for company's resources management. The process of portfolio construction through a combination of individual investment projects aims to optimize available resources utilization. Currently, there is no single approach to determining the optimal portfolio, since portfolio requirements depend on a particular company. Due to its simplicity in terms of implementation, approaches based on the ranking of projects according to individual technical and economic indicators are widely used. However, if a company needs to take into account several indicators, then this approach is not applicable. An effective solution to this problem is to create a technique that allows to find Pareto-optimal portfolios. In this work, the problem statement and analysis of existing solving approaches were carried out. Based on that, methodology of scenario modeling of a company's portfolio was created. The posed problem belongs to the class of NP-hard problems and for its efficient solving it is necessary to use metaheuristic algorithms. These algorithms include genetic algorithms, variable neighborhood search, bees algorithm, etc. Genetic algorithm NSGA-II is widely used for solving multi-objective optimization problems due to its high performance and ease of use. This algorithm was adapted to solve proposed problem. Based on it, a methodology was developed for the formation of Pareto-optimal investment portfolios, taking into account financial and infrastructural restrictions. Testing of the methodology was carried out on a dataset of synthetic projects simulating real investment projects. Various combinations of this projects simulated investment portfolios. The optimization objectives were maximization of net present value (NPV), maximization of cumulative oil production and minimization of total investments. In order to control the quality of the results obtained using genetic algorithm, the problem was also solved by using variable neighborhood search. However, the quality of the results obtained using genetic algorithm was higher. Analysis of the results allows us to consider the use of multi-objective optimization as a tool for forming a pool of investment portfolios, which provides flexible company management. In the proposed work, it is shown that the problem of scenario-based modeling of company's investment portfolio can be considered as a multi-objective optimization problem. Based on the existing metaheuristic algorithms, a Pareto-optimal investment portfolio search methodology has been developed. Proposed methodology can effectively solve the problem of managing company resources.

The goal of the multi-objective optimization algorithm is to quickly and accurately find a set of trade-off solutions. This paper develops a clustering-based competitive multi-objective particle swarm optimizer using the enhanced grid for solving multi-objective optimization problems, named EGC-CMOPSO. The enhanced grid mechanism involved in EGC-CMOPSO is designed to locate superior Pareto optimal solutions. Subsequently, a hierarchical-based clustering is established on the grid for improving the accuracy rate of the grid selection. Due to the adaptive division of clustering centers, EGC-CMOPSO is applicable for solving MOPs with various Pareto front (PF) shapes. Particularly, the inferior solutions are discarded and the leading particles are identified by the comprehensive ranking of particles in each cluster. Finally, the selected leading particles compete against each other, and the winner guides the update of the current particle. The proposed EGC-CMOPSO and the eight latest multi-objective optimization algorithms are performed on 21 test problems. The experimental results validate that the proposed EGC-CMOPSO is capable of handling multi-objective optimization problems (MOPs) and obtaining superior performance on both convergence and diversity.

Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.