SI-based Algorithms Research Articles

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

Due to the volatility and uncertainty of the financial market, investors often use the form of portfolio to actively manage their assets. Portfolio optimization (PO) is becoming more and more important for investors. However, PO is frequently a kind of NP-hard problem in the field of modern financial optimization, which has gradually attracted the attention and interest of researchers. Some efficient mathematical models were built to describe the return and risk of portfolio. A lot of precise and approximate fast algorithms are used to solve the established PO models. The fundamental purpose is to maximize the return and to minimize the risk of portfolio under certain constraints. In recent years, researchers not only limit the goal of PO to the balance between risk and return, but also pay attention to liquidity, environmental, social, and governance (ESG) controversy level, Sortino ratio, and other indicators. The number of PO targets and constraints is further extended. In the past two decades, swarm intelligence (SI) algorithms have been widely introduced to solve PO problems. SI algorithm is mainly inspired from the daily phenomena in nature or self-organization, self-adaptation, and self-learning of biological population. The existing research results show that SI algorithm has the characteristics of high efficiency and can obtain satisfactory solutions in solving PO problems. The recent advances on the classic portfolio optimization concepts, models, and the usual SI-based solving algorithms are presented. Finally, future potential research directions are presented.

A Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems

In the last two decades, the field of global optimization has become very active, and, in this regard, many deterministic and stochastic algorithms were developed for solving various optimization problems. Among them, swarm intelligence (SI) is a stochastic algorithm that is more flexible and robust and has had the ability to find an optimum solution for high-dimensional optimization and search problems. SI-based algorithms are mainly inspired by the social behavior of fish schooling or bird flocking. Among the SI-based algorithms, Bat algorithm (BA) is one of the recently developed evolutionary algorithms. It employs an echolocation behavior of microbats by varying pulse rates of emission and loudness to perform their search process. In this paper, a modified Bat algorithm (MBA) is developed. The main focus of the MBA is to further enhance the exploration and exploitation search abilities of the original Bat algorithm. The performance of the modified Bat algorithm (MBA) is examined over the benchmark functions designed for evolutionary algorithms competition in the special session of 2005 IEEE Congress on Evolutionary Computation. The used benchmark functions include the unimodal, multimodal, and hybrid benchmark functions with high dimensionality. Furthermore, the impact analysis with respect to different values of temperatures is conducted by executing the proposed algorithm twenty-five times independently by using each benchmark function with different random seeds.

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.

Swarm intelligence versus direct cover algorithms in synthesis of Multi-Valued Logic functions

Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.

Swarm intelligence based centralized clustering: a novel solution

Recent evolutions of MEMS technology, digital electronics, and wireless communication technologies have made smart environments possible; especially through the apparition or incorporation of sensors. Despite being small-sized, the sensors have paved the way for data collection in the environments where they are applied; including luminosity, gas presence, water content, humidity, pressure, and temperature. This research aims to pragmatically optimize Wireless Sensor Network Localization and Network Coverage issues using nature-inspired algorithms. The specific objective is to establish an optimal nature-inspired algorithm and comparing it with other algorithms regarding the capacity to achieve manufacturing optimization in large WSNs, especially in relation to the parameters of high scalability, data delivery rate, and low-energy consumption. Also, the study seeks to determine the extent to which swarm intelligence (SI)-based centralized clustering solutions (optimal nature-inspired algorithms), compared to other approaches, might optimize the WSN features of localization and network coverage. To determine the solutions’ performance, the study involved three scenarios. In scenario 1, the network operational time and the stability of SI-based algorithms were investigated for large WSNs that had the minimum heterogeneity. Imperative to note is that the WSNs on focus had different numbers of nodes, which included 500, 300, and 100. In scenario 2, the motivation was to investigate the SI-based WSN protocols in relation to the parameters of packet delivery, energy conception, and network lifetime for large WSNs. In scenario 3, the performance of SI-based solutions over large WSNs was compared to that which had been reported previously for other algorithms; with the target parameters of comparison being attributes such as packet delivery, energy conception, and network lifetime. From the findings, this study established that SI-based centralized clustering solutions are not only more recent but also exhibit superior performance compared to other algorithms; with the parameters of the amount of data delivered to the BS, energy consumption and scalability on the focus.

Teaching learning-based optimisation algorithm: a survey

In recent years swarm intelligence (SI)-based techniques have proven their importance for finding the solution of global optimisation problems. In SI-based algorithms agents act in a group and learn from each other for food foraging surviving, etc. Teaching-learning-based optimisation algorithm (TLBOA) is an efficient approach of dealing with linear, nonlinear and multi-dimensional optimisation problems established by Dr. R. Venkata Rao in 2011. Since its inception, a lot of research has been carried out to make TLBOA more proficient and to apply it to different types of optimisation problems. This paper presents a review of TLBOA developments, applications, comparative-performance, and future research perspectives.

Grasshopper inspired artificial bee colony algorithm for numerical optimisation

ABSTRACT Swarm intelligence (SI)-based algorithms are performing very well in the field of optimisation over the past few decades. A lot of new SI-based algorithms are being developed. The existing algorithms are also modified, mostly, either by hybridising them with some other algorithms or by incorporating local search techniques. This research presents a new local search strategy based on grasshopper (GH) jumping mechanism. The proposed local search strategy is termed as GH local search strategy. Further, the proposed strategy is incorporated into an efficient SI-based algorithm, artificial bee colony (ABC) algorithm. The proposed hybridised algorithm is termed as GH inspired ABC (GHABC) algorithm. The proposed GHABC is tested on 37 numerical benchmark optimisation functions. The results indicate that the proposed GHABC algorithm is a competent approach for solving numerical optimisation problems.

Swarm intelligence: past, present and future

Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.

Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks

Swarm intelligence (SI)-based metaheuristics are well applied to solve real-time optimization problems of efficient node clustering and energy-aware data routing in wireless sensor networks. This paper presents another superior approach for these optimization problems based on an artificial bee colony metaheuristic. The proposed clustering algorithm presents an efficient cluster formation mechanism with improved cluster head selection criteria based on a multi-objective fitness function, whereas the routing algorithm is devised to consume minimum energy with least hop-count for data transmission. Extensive evaluation and comparison of the proposed approach with existing well-known SI-based algorithms demonstrate its superiority over others in terms of packet delivery ratio, average energy consumed, average throughput and network life.

Swarm intelligence for natural language processing

Natural language processing NLP is an area dealing with computational methods for achieving human-like language processing. Traditionally, NLP research has been focused on developing efficient and robust algorithms to treat most NLP tasks, including syntactic and semantic analysis, grammar induction, summary and text generation, document clustering and machine translation. Swarm intelligence SI methods are effective to do so, since they have been successfully applied for many real-world problems. Recently, NLP and SI have been active areas of research, joined together more than once to solve problems in NLP field. This paper presents a review of recent developments of SI methods in NLP. It shows that only a few NLP tasks and applications were tackled by using SI-based algorithms. These mainly include text document clustering and classification, text summarisation, word sense disambiguation, information retrieval, and speaker recognition. This study also shows that four SI-based algorithms were examined in NLP field, including ant colony optimisation ACO, particle swarm optimisation PSO, bee swarm optimisation BSO, and firefly algorithm FA, emphasising ACO and PSO as the most investigated algorithms in this field.

Particle bee algorithm for tower crane layout with material quantity supply and demand optimization

Abstract The tower crane layout (TCL) problem, a typical construction site layout (CSL) problem, is currently used in a wide range of construction projects and site conditions. The tower crane is a key facility used in the vertical and horizontal transportation of materials, particularly heavy prefabrication units such as steel beams, ready-mixed concrete, prefabricated elements, and large-panel formwork. Matching the location of tower cranes to material supply and engineering demands is a combinatorial optimization issue within the TCL problem that is difficult to resolve. Swarm intelligence (SI) is a popular artificial intelligence technique that is used widely to resolve complex optimization problems. Various SI-based algorithms have been developed that emulate the collective behavior of animals such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study applies the particle bee algorithm (PBA), a hybrid swarm algorithm that integrates the respective advantages of honey bee and bird swarms, to the TCL problem. The performances of PBA, BA, and PSO are compared in terms of their effectiveness in resolving a practical TCL problem in construction engineering. Results show that the PBA performs better than both the BA and PSO algorithms.

Swarm intelligence based algorithms: a critical analysis

Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We also analyze the ways of achieving exploration and exploitation in algorithms by using mutation, crossover and selection. In addition, we also look at algorithms using dynamic systems, self-organization and Markov chain framework. Finally, we provide some discussions and topics for further research.

Particle swarm optimizer with adaptive tabu and mutation

Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) are widely used to tackle black-box global optimization problems when no prior knowledge is available. In order to increase search diversity and avoid stagnation in local optima, the mutation operator was introduced and has been extensively studied in EAs and SI-based algorithms. However, the performance after introducing mutation can be affected in many aspects and the parameters used to perform mutations are very hard to determine. For the purpose of developing efficient mutation operators, this article proposes a unified tabu and mutation framework with parameter adaptations in the context of the Particle Swarm Optimizer (PSO). The proposed framework is a significant extension of our preliminary work [Wang et al. 2007]. Empirical studies on 25 benchmark functions indicate that under the proposed framework: (1) excellent performance can be achieved even with a small number of mutations; (2) the derived algorithm consistently performs well on diverse types of problems and overall performance even surpasses the state-of-the-art PSO variants and representative mutation-based EAs; and (3) fast convergence rates can be preserved despite the use of a long jump mutation operator (the Cauchy mutation).

Radio-detector minerals

The tower crane layout (TCL) problem, a typical construction site layout (CSL) problem, is currently used in a wide range of construction projects and site conditions. The tower crane is a key facility used in the vertical and horizontal transportation of materials, particularly heavy prefabrication units such as steel beams, ready-mixed concrete, prefabricated elements, and large-panel formwork. Matching the location of tower cranes to material supply and engineering demands is a combinatorial optimization issue within the TCL problem that is difficult to resolve. Swarm intelligence (SI) is a popular artificial intelligence technique that is used widely to resolve complex optimization problems. Various SI-based algorithms have been developed that emulate the collective behavior of animals such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study applies the particle bee algorithm (PBA), a hybrid swarm algorithm that integrates the respective advantages of honey bee and bird swarms, to the TCL problem. The performances of PBA, BA, and PSO are compared in terms of their effectiveness in resolving a practical TCL problem in construction engineering. Results show that the PBA performs better than both the BA and PSO algorithms.