A New Bio-Inspired Social Spider Algorithm

2021 ◽  
Vol 12 (1) ◽  
pp. 79-93
Author(s):  
Dharmpal Singh
Keyword(s):  
Swarm Intelligence ◽  
Real World ◽  
Optimization Problems ◽  
Fitness Function ◽  
Foraging Strategy ◽  
Superior Performance ◽  
Experimental Result ◽  
Social Spider ◽  
Spider Web ◽  
Social Spider Algorithm

The concept of bio-inspired algorithms is used in real-world problems to search the efficient problem-solving methods. Evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques of metahuristics. In this paper, an effort has been made to propose a modified social spider algorithm to solve global optimization problems in the real world. Social spiders used the foraging strategy, vibrations on the spider web to determine the positions of prey. The selection of vibration, estimated new position and calculation of the fitness function, has been furnished in details way as compared to different previously proposed swarm intelligence algorithms. Moreover, experimental result has been carried out by modified social spider on series of widely-used benchmark problem with four benchmark algorithms. Furthermore, a modified form of the proposed algorithm has superior performance as compared to other state-of-the-art metaheuristics algorithms.

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

2021 ◽  
pp. 2150020
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Traveling Salesman ◽  
The Other ◽  
Discrete Optimization Problem ◽  
Social Spider ◽  
Social Spider Algorithm

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

scholarly journals A novel methodology for optimal land allocation for agricultural crops using Social Spider Algorithm

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7559
Author(s):  
N Thilagavathi ◽  
T Amudha
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Crop Productivity ◽  
Research Field ◽  
Land Allocation ◽  
Net Income ◽  
Social Spider ◽  
Combinatorial Optimization Problems ◽  
Social Spider Algorithm ◽  
Land Holdings

In the current agricultural scenario, availability of suitable land for cultivation is less and profitable allocation of the land for cultivating crops seems to be a cumbersome task. Crop planning optimization is a major research field in agriculture, in which land optimization is a significant challenge, which falls under the category of combinatorial optimization problems. The main objective of the present research is to maximize the net income from agriculture through optimal land allocation. Bio-inspired algorithms are quite popular in solving combinatorial optimization problems. Social Spider Algorithm (SSA), a new bio-inspired algorithm, is used to solve land optimization problem in this research based on the simulation of cooperative behaviour of social spiders. The agricultural area chosen for case study is the Coimbatore region, located in Tamilnadu state, India and the relevant data for the crops are collected from Tamilnadu Agricultural University Coimbatore, India. The optimal planting area, crop productivity for various land holdings and the water requirements are computed by SSA and the results have shown better directions for agricultural planning to improve the profit with constrained land area and water limitations.

An evolutionary based framework for many-objective optimization problems

2018 ◽  
Vol 35 (4) ◽  
pp. 1805-1828 ◽  
Author(s):  
Kimia Bazargan Lari ◽  
Ali Hamzeh
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Population Diversity ◽  
Numerical Results ◽  
Superior Performance ◽  
High Dimensional ◽  
Test Problems ◽  
Content Type ◽  
Objective Space

Purpose Recently, many-objective optimization evolutionary algorithms have been the main issue for researchers in the multi-objective optimization community. To deal with many-objective problems (typically for four or more objectives) some modern frameworks are proposed which have the potential of achieving the finest non-dominated solutions in many-objective spaces. The effectiveness of these algorithms deteriorates greatly as the problem’s dimension increases. Diversity reduction in the objective space is the main reason of this phenomenon. Design/methodology/approach To properly deal with this undesirable situation, this work introduces an indicator-based evolutionary framework that can preserve the population diversity by producing a set of discriminated solutions in high-dimensional objective space. This work attempts to diversify the objective space by proposing a fitness function capable of discriminating the chromosomes in high-dimensional space. The numerical results prove the potential of the proposed method, which had superior performance in most of test problems in comparison with state-of-the-art algorithms. Findings The achieved numerical results empirically prove the superiority of the proposed method to state-of-the-art counterparts in the most test problems of a known artificial benchmark. Originality/value This paper provides a new interpretation and important insights into the many-objective optimization realm by emphasizing on preserving the population diversity.

Multi-objective fatigue life optimization using Tabu Genetic Algorithms

2015 ◽  
Vol 6 (6) ◽  
pp. 677-688
Author(s):  
Kim C. Long ◽  
William S Duff ◽  
John W Labadie ◽  
Mitchell J Stansloski ◽  
Walajabad S Sampath ◽  
...  
Keyword(s):  
Tabu Search ◽  
Real World ◽  
Optimization Problem ◽  
High Efficiency ◽  
Optimization Problems ◽  
Superior Performance ◽  
Bench Mark ◽  
Reliability Optimization ◽  
Content Type ◽  
Multi Objective

Purpose – The purpose of this paper is to present a real world application of an innovative hybrid system reliability optimization algorithm combining Tabu search with an evolutionary algorithm (TSEA). This algorithm combines Tabu search and Genetic algorithm to provide a more efficient search method. Design/methodology/approach – The new algorithm is applied to an aircraft structure to optimize its reliability and maintain its structural integrity. For retrofitting the horizontal stabilizer under severe stall buffet conditions, a decision support system (DSS) is developed using the TSEA algorithm. This system solves a reliability optimization problem under cost and configuration constraints. The DSS contains three components: a graphical user interface, a database and several modules to provide the optimized retrofitting solutions. Findings – The authors found that the proposed algorithm performs much better than state-of-the-art methods such as Strength Pareto Evolutionary Algorithms on bench mark problems. In addition, the proposed TSEA method can be easily applied to complex real world optimization problem with superior performance. When the full combination of all input variables increases exponentially, the DSS become very efficient. Practical implications – This paper presents an application of the TSEA algorithm for solving nonlinear multi-objective reliability optimization problems embedded in a DSS. The solutions include where to install doublers and stiffeners. Compromise programming is used to rank all non-dominant solutions. Originality/value – The proposed hybrid algorithm (TSEA) assigns fitness based upon global dominance which ensures its convergence to the non-dominant front. The high efficiency of this algorithm came from using Tabu list to guidance the search to the Pareto-optimal solutions.

scholarly journals The novel Social Spider Optimization Algorithm: Overview, Modifications, and Applications

2021 ◽  
Vol 5 (2) ◽  
pp. 32-51
Author(s):  
Saman Almufti
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Biological Behavior ◽  
The Novel ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Computer Scientists ◽  
Problem Solving Strategies ◽  
Cooperative Behaviors ◽  
Real World Problems

The continues in real-world problems increasing  complexity motivated computer scientists and researchers to search for more-efficient problem-solving strategies. Generally  natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization problems because of their ability to adjust to variety of conditions. This paper present a  swarm based algorithm that is based on the cooperative behaviors between social spider, it called Social Spider Optimization (SSO) algorithm. In SSO, search agents characterize a set of spiders which together move according to a biological behavior in colony. During the past years after SSO introduction, many modifications has improved the performance of the algorithm and has been applied in several fields. In this paper, the improvements, and applications of the SSO are reviewed.

scholarly journals Social Spider Optimization Algorithm: Theory and its Applications

2019 ◽  
Vol 8 (10) ◽  
pp. 327-332
Keyword(s):  
Neural Networks ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Basic Sciences ◽  
Insect Behaviour ◽  
Social Spider ◽  
Electric Circuits ◽  
Social Spider Optimization ◽  
Algorithm Theory ◽  
Swarm Intelligence Algorithm

An extensive variety of optimization problems are solved by swarm intelligence algorithms that are modelled based on the animal or insect behaviour while living in groups. One such recent swarm intelligence algorithm is Social Spider Optimization (SSO). This paper thoroughly reviews and analyses the characteristics of this meta-heuristic algorithm. Since the existing literature of this algorithm is comparatively limited, the paper discusses the research ideas presented in such existing works and classifies the literature on basis of the application areas like image processing, optical flow, electric circuits, neural networks and basic sciences. It also sets a basis for research applications of the algorithm in order to tap the complete potential of the algorithm in other areas to achieve desired results

An Adaptive Aggregation-Based Approach for Expensively Constrained Black-Box Optimization Problems

2018 ◽  
Vol 140 (9) ◽  
Author(s):  
George H. Cheng ◽  
Timothy Gjernes ◽  
G. Gary Wang
Keyword(s):  
Design Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Trust Region ◽  
Adaptive Strategy ◽  
Black Box ◽  
Superior Performance ◽  
Adaptive Aggregation ◽  
Novel Approach ◽  
Single Objective

Expensive constraints are commonly seen in real-world engineering design. However, metamodel based design optimization (MBDO) approaches often assume inexpensive constraints. In this work, the situational adaptive Kreisselmeier and Steinhauser (SAKS) method was employed in the development of a hybrid adaptive aggregation-based constraint handling strategy for expensive black-box constraint functions. The SAKS method is a novel approach that hybridizes the modeling and aggregation of expensive constraints and adds an adaptive strategy to control the level of hybridization. The SAKS strategy was integrated with a modified trust region-based mode pursuing sampling (TRMPS) algorithm to form the SAKS-trust region optimizer (SAKS-TRO) for single-objective design optimization problems with expensive black-box objective and constraint functions. SAKS-TRO was benchmarked against five popular constrained optimizers and demonstrated superior performance on average. SAKS-TRO was also applied to optimize the design of an industrial recessed impeller.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

Review of Network Flow Algorithms David P. Williamson

2021 ◽  
Vol 52 (1) ◽  
pp. 12-15
Author(s):  
S.V. Nagaraj
Keyword(s):  
Graduate Students ◽  
Real World ◽  
Network Flow ◽  
Network Flows ◽  
Optimization Problems ◽  
Capacity Constraints ◽  
Incoming Flow ◽  
Network Flow Problems ◽  
Flow Problems ◽  
Real World Problems

This book is on algorithms for network flows. Network flow problems are optimization problems where given a flow network, the aim is to construct a flow that respects the capacity constraints of the edges of the network, so that incoming flow equals the outgoing flow for all vertices of the network except designated vertices known as the source and the sink. Network flow algorithms solve many real-world problems. This book is intended to serve graduate students and as a reference. The book is also available in eBook (ISBN 9781316952894/US$ 32.00), and hardback (ISBN 9781107185890/US$99.99) formats. The book has a companion web site www.networkflowalgs.com where a pre-publication version of the book can be downloaded gratis.

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