Harris hawks optimization: Algorithm and applications

A recent set of overused population-based methods have been published in recent years. Despite their popularity, most of them have uncertain, immature performance, partially done verifications, similar overused metaphors, similar immature exploration and exploitation components and operations, and an insecure tradeoff between exploration and exploitation trends in most of the new real-world cases. Therefore, all users need to extensively modify and adjust their operations based on main evolutionary methods to reach faster convergence, more stable balance, and high-quality results. To move the optimization community one step ahead toward more focus on performance rather than change of metaphor, a general-purpose population-based optimization technique called Hunger Games Search (HGS) is proposed in this research with a simple structure, special stability features and very competitive performance to realize the solutions of both constrained and unconstrained problems more effectively. The proposed HGS is designed according to the hunger-driven activities and behavioural choice of animals. This dynamic, fitness-wise search method follows a simple concept of “Hunger” as the most crucial homeostatic motivation and reason for behaviours, decisions, and actions in the life of all animals to make the process of optimization more understandable and consistent for new users and decision-makers. The Hunger Games Search incorporates the concept of hunger into the feature process; in other words, an adaptive weight based on the concept of hunger is designed and employed to simulate the effect of hunger on each search step. It follows the computationally logical rules (games) utilized by almost all animals and these rival activities and games are often adaptive evolutionary by securing higher chances of survival and food acquisition. This method's main feature is its dynamic nature, simple structure, and high performance in terms of convergence and acceptable quality of solutions, proving to be more efficient than the current optimization methods. The effectiveness of HGS was verified by comparing HGS with a comprehensive set of popular and advanced algorithms on 23 well-known optimization functions and the IEEE CEC 2014 benchmark test suite. Also, the HGS was applied to several engineering problems to demonstrate its applicability. The results validate the effectiveness of the proposed optimizer compared to popular essential optimizers, several advanced variants of the existing methods, and several CEC winners and powerful differential evolution (DE)-based methods abbreviated as LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods in handling many single-objective problems. We designed this open-source population-based method to be a standard tool for optimization in different areas of artificial intelligence and machine learning with several new exploratory and exploitative features, high performance, and high optimization capacity. The method is very flexible and scalable to be extended to fit more form of optimization cases in both structural aspects and application sides. This paper's source codes, supplementary files, Latex and office source files, sources of plots, a brief version and pseudocode, and an open-source software toolkit for solving optimization problems with Hunger Games Search and online web service for any question, feedback, suggestion, and idea on HGS algorithm will be available to the public at https://aliasgharheidari.com/HGS.html .

Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts

Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

Metaheuristic research: a comprehensive survey

This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of Crocodile behaviour are implemented, such as encircling, which is performed by high walking or belly walking, and hunting, which is performed by hunting coordination or hunting cooperation. The mentioned search methods of the proposed RSA are unique compared to other existing algorithms. The performance of the proposed RSA is evaluated using twenty-three classical test functions, thirty CEC2017 test functions, ten CEC2019 test functions, and seven real-world engineering problems. The obtained results of the proposed RSA are compared to various existing optimization algorithms in the literature. The results of the tested three benchmark functions revealed that the proposed RSA achieved better results than the other competitive optimization algorithms. The results of the Friedman ranking test proved that the RSA is a significantly superior method than other comparative methods. Finally, the results of the examined engineering problems showed that the RSA obtained better results compared to other various methods.

Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer

This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of Crocodile behaviour are implemented, such as encircling, which is performed by high walking or belly walking, and hunting, which is performed by hunting coordination or hunting cooperation. The mentioned search methods of the proposed RSA are unique compared to other existing algorithms. The performance of the proposed RSA is evaluated using twenty-three classical test functions, thirty CEC2017 test functions, ten CEC2019 test functions, and seven real-world engineering problems. The obtained results of the proposed RSA are compared to various existing optimization algorithms in the literature. The results of the tested three benchmark functions revealed that the proposed RSA achieved better results than the other competitive optimization algorithms. The results of the Friedman ranking test proved that the RSA is a significantly superior method than other comparative methods. Finally, the results of the examined engineering problems showed that the RSA obtained better results compared to other various methods. Source codes of RSA are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/101385-reptile-search-algorithm-rsa-a-nature-inspired-optimizer • Developed a novel optimization algorithm inspired by hunting behaviour of Reptiles (RSA). • Tested RSA against classical, CEC2017, CEC2019 test functions and engineering problems. • Compared the RSA to other well-known optimization algorithms. • Demonstrated effectiveness and superiority of the proposed RSA. 

Passing vehicle search (PVS): A novel metaheuristic algorithm

Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics

Effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO)

Three modified versions of passing vehicle search (PVS) are proposed and tested on truss topology optimization with static and dynamic constraints. PVS works on the mechanism of passing a vehicle on a two-lane highway. The heuristic nature of PVS allows the search to jump into non-visited regions (exploration) and also permits a local search of visited regions (exploitation). First, the original PVS algorithm is improved to avoid a local optima trap using a novel parallel run mechanism. Then, population diversity is improved by incorporating the selection of simulated annealing. The various versions of PVS are verified on the truss design problems. Comparative results show that the parallel run concept improves the original PVS algorithm. The selection using the Boltzmann probability as used in simulated annealing further improves the algorithm.

Topology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithms

In this paper, seven different metaheuristic optimization algorithms developed between 2005 and 2012 are applied to optimize the robot trajectory for a three-revolute (3R) robotic arm. These algorithms include the artificial bee colony (ABC) algorithm, biogeography-based optimization, gravitational search algorithm, cuckoo search algorithm (CS), firefly algorithm, bat algorithm, and teaching–learning-based optimization (TLBO). This work presents the optimization problem with the objective to plan a trajectory, which can minimize joint travelling time, joint travelling distance, and total joint Cartesian lengths, simultaneously. Nine different design variables are considered for the intermediate joint angles, joint velocities, the end-effector angle for the final configuration, the time from initial to intermediate configuration, and the time from intermediate to final configuration. Two different experiments are conducted to investigate the effect of different considered metaheuristics: 1) for the free workspace and 2) for the workspace with obstacle. All these algorithms are compared based on the algorithm ability to find the best solutions, mean solutions, standard deviation, computational effort, and the convergence of the solutions. Statistical investigation is also performed by using the Friedman rank test and the Holm–Sidak multiple comparison $t$ - test to check the significance of the algorithm for its suitability to the robot trajectory optimization problems. The results show the significance of TLBO, ABC, and CS for the robot trajectory optimization problems.

Poonam Savsani

Papers

Passing vehicle search (PVS): A novel metaheuristic algorithm

Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics

Effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO)

Topology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithms

Comparative Study of Different Metaheuristics for the Trajectory Planning of a Robotic Arm