scholarly journals Improved Sparrow Search Algorithm Based on Iterative Local Search

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Shaoqiang Yan ◽  
Ping Yang ◽  
Donglin Zhu ◽  
Wanli Zheng ◽  
Fengxuan Wu
Keyword(s):  
Local Search ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Search Performance ◽  
Search Range ◽  
Variable Helix ◽  
Local Optimal Solution ◽  
Local Search Strategy

This paper solves the shortcomings of sparrow search algorithm in poor utilization to the current individual and lack of effective search, improves its search performance, achieves good results on 23 basic benchmark functions and CEC 2017, and effectively improves the problem that the algorithm falls into local optimal solution and has low search accuracy. This paper proposes an improved sparrow search algorithm based on iterative local search (ISSA). In the global search phase of the followers, the variable helix factor is introduced, which makes full use of the individual’s opposite solution about the origin, reduces the number of individuals beyond the boundary, and ensures the algorithm has a detailed and flexible search ability. In the local search phase of the followers, an improved iterative local search strategy is adopted to increase the search accuracy and prevent the omission of the optimal solution. By adding the dimension by dimension lens learning strategy to scouters, the search range is more flexible and helps jump out of the local optimal solution by changing the focusing ability of the lens and the dynamic boundary of each dimension. Finally, the boundary control is improved to effectively utilize the individuals beyond the boundary while retaining the randomness of the individuals. The ISSA is compared with PSO, SCA, GWO, WOA, MWOA, SSA, BSSA, CSSA, and LSSA on 23 basic functions to verify the optimization performance of the algorithm. In addition, in order to further verify the optimization performance of the algorithm when the optimal solution is not 0, the above algorithms are compared in CEC 2017 test function. The simulation results show that the ISSA has good universality. Finally, this paper applies ISSA to PID parameter tuning and robot path planning, and the results show that the algorithm has good practicability and effect.

Diagnostic Strategy Optimization Based on Predatory Search Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 2441-2444
Author(s):  
Jin Luo ◽  
Qi Bin Deng ◽  
Chen Meng
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Global Search ◽  
State Probability ◽  
Diagnostic Strategy ◽  
Local Optimal Solution ◽  
Predatory Search Algorithm

With respect to the inherent NP-hard complexity of Optimization of testability diagnostic strategy problem, a predatory search algorithm simulating animal predatory strategies was designed. This algorithm adopted the gross test expense including state probability, isolation matrix and test expense as its objective function, defined local and global search by the restriction value of search space based on two points exchange, and realized the conversion between local and global search by adjusting the restriction value of search space. It had better ability to conduct local search and jump out of local optimal solution simultaneously, and provided a better resolution for the optimization of testability diagnostic strategy.

scholarly journals Chaotic Search Based Equilibrium Optimizer for Dealing with Nonlinear Programming and Petrochemical Application

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 200
Author(s):  
Abd Allah A. Mousa ◽  
Mohammed A. El-Shorbagy ◽  
Ibrahim Mustafa ◽  
Hammad Alotaibi
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Phase I ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Limited Area ◽  
Search Performance ◽  
Specific Chemical ◽  
Chaotic Search

In this article, chaotic search based constrained equilibrium optimizer algorithm (CS-CEOA) is suggested by integrating a novel heuristic approach called equilibrium optimizer with a chaos theory-based local search algorithm for solving general non-linear programming. CS-CEOA is consists of two phases, the first one (phase I) aims to detect an approximate solution, avoiding being stuck in local minima. In phase II, the chaos-based search algorithm improves local search performance to obtain the best optimal solution. For every infeasible solution, repair function is implemented in a way such that, a new feasible solution is created on the line segment defined by a feasible reference point and the infeasible solution itself. Due to the fast globally converging of evolutionary algorithms and the chaotic search’s exhaustive search, CS-CEOA could locate the true optimal solution by applying an exhaustive local search for a limited area defined from Phase I. The efficiency of CS-CEOA is studied over multi-suites of benchmark problems including constrained, unconstrained, CEC’05 problems, and an application of blending four ingredients, three feed streams, one tank, and two products to create some certain products with specific chemical properties, also to satisfy the target costs. The results were compared with the standard evolutionary algorithms as PSO and GA, and many hybrid algorithms in the same simulation environment to approve its superiority of detecting the optimal solution over selected counterparts.

scholarly journals Lens Learning Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chengtian Ouyang ◽  
Donglin Zhu ◽  
Yaxian Qiu
Keyword(s):  
Path Planning ◽  
Simulated Annealing Algorithm ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Improved Algorithm

In this paper, a lens learning sparrow search algorithm (LLSSA) is proposed to improve the defects of the new sparrow search algorithm, which is random and easy to fall into local optimum. The algorithm has achieved good results in function optimization and has planned a safer and less costly path to the three-dimensional UAV path planning. In the discoverer stage, the algorithm introduces the reverse learning strategy based on the lens principle to improve the search range of sparrow individuals and then proposes a variable spiral search strategy to make the follower's search more detailed and flexible. Finally, it combines the simulated annealing algorithm to judge and obtain the optimal solution. Through 15 standard test functions, it is verified that the improved algorithm has strong search ability and mining ability. At the same time, the improved algorithm is applied to the path planning of 3D complex terrain, and a clear, simple, and safe route is found, which verifies the effectiveness and practicability of the improved algorithm.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

Modified Sub-Population Based Heat Transfer Search Algorithm for Structural Optimization

2017 ◽  
Vol 8 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Ghanshyam Tejani ◽  
Vimal Savsani ◽  
Vivek Patel
Keyword(s):  
Heat Transfer ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Population Based ◽  
Truss Optimization ◽  
Feasible Region ◽  
Frequency Constraints ◽  
Local Optimal Solution ◽  
Better Than

In this study, a modified heat transfer search (MHTS) algorithm is proposed by incorporating sub-population based simultaneous heat transfer modes viz. conduction, convection, and radiation in the basic HTS algorithm. However, the basic HTS algorithm considers only one of the modes of heat transfer for each generation. The multiple natural frequency constraints in truss optimization problems can improve the dynamic behavior of the structure and prevent undesirable vibrations. However, shape and size variables subjected to frequency constraints are difficult to handle due to the complexity of its feasible region, which is non-linear, non-convex, implicit, and often converging to the local optimal solution. The viability and effectiveness of the HTS and MHTS algorithms are investigated by six standard trusses problems. The solutions illustrate that the MHTS algorithm performs better than the HTS algorithm.

Design of 3D-Route Planning Based on the Improvement of Simple Genetic Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1690-1694
Author(s):  
Jian Xia Zhang ◽  
Tao Yu ◽  
Ji Ping Chen ◽  
Ying Hao Lin ◽  
Yu Meng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Scientific Research ◽  
Route Planning ◽  
Optimal Solution ◽  
Global Search ◽  
Global Search Algorithm ◽  
Simple Genetic Algorithm ◽  
Local Optimal Solution ◽  
Simulation Results

With the wide application of UAV in the scientific research,its route planning is becoming more and more important. In order to design the best route planning when UAV operates in the field, this paper mainly puts to use the simple genetic algorithm to design 3D-route planning. It primarily introduces the advantages of genetic algorithm compared to others on the designing of route planning. The improvement of simple genetic algorithm is because of the safety of UAV when it flights higher, and the 3D-route planning should include all the corresponding areas. The simulation results show that: the improvement of simple genetic algorithm gets rid of the dependence of parameters, at the same time it is a global search algorithm to avoid falling into the local optimal solution. Whats more, it can meet the requirements of the 3D-route planning design, to the purpose of regional scope and high safety.

Collision Avoidance in Multiple-Ship Situations by Distributed Local Search

2014 ◽  
Vol 18 (5) ◽  
pp. 839-848 ◽  
Author(s):  
Dong-Gyun Kim ◽  
◽  
Katsutoshi Hirayama ◽  
Gyei-Kark Park ◽  
Keyword(s):  
Local Search ◽  
Collision Avoidance ◽  
Search Algorithm ◽  
Fuzzy Theory ◽  
Search Performance ◽  
Local Search Algorithm ◽  
Collision Risk ◽  
Mode Of Transportation ◽  
Ship Collision ◽  
Approach Time

As vital transportation carriers in trade, ships have the advantage of stability, economy, and bulk capacity over airplanes, trucks, and trains. Even so, their loss and cost due to collisions and other accidents exceed those of any other mode of transportation. To prevent ship collisions many ways have been suggested, e.g., the 1972 COLREGs which is the regulation for preventing collision between ships. Technologically speaking, many related studies have been conducted. The term “Ship domain” involves that area surrounding a ship that the navigator wants to keep other ships clear of. Ship domain alone is not sufficient, however, for enabling one or more ships to simultaneously determine the collision risk for all of the ships concerned. Fuzzy theory is useful in helping ships avoid collision in that fuzzy theory may define whether collision risk is based on distance to closest point of approach, time to closest point of approach, or relative bearing – algorithms that are difficult to apply to more than one ships at one time. The main purpose of this study is thus to reduce collision risk among multiple ships using a distributed local search algorithm (DLSA). By exchanging information on, for example, next-intended courses within a certain area among ships, ships having the maximum reduction in collision risk change courses simultaneously until all ships approach a destination without collision. In this paper, we introduce distributed local search and explain how it works using examples. We conducted experiments to test distributed local search performance for certain instances of ship collision avoidance. Experiments results showed that in most cases, our proposal applies well in ship collision avoidance amongmultiple ships.

scholarly journals Real power loss diminution by predestination of particles wavering search algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 92
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Real Power ◽  
Gradient Based ◽  
Local Optimal Solution ◽  
Simulation Results ◽  
Power Problem ◽  
Steady Velocity

In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion.  Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.

scholarly journals A Learning Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Chengtian Ouyang ◽  
Donglin Zhu ◽  
Fengqi Wang
Keyword(s):  
Path Planning ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Method ◽  
Search Process ◽  
Local Optimum ◽  
Test Function ◽  
Intelligent Optimization Algorithms ◽  
Robot Path

This paper solves the drawbacks of traditional intelligent optimization algorithms relying on 0 and has good results on CEC 2017 and benchmark functions, which effectively improve the problem of algorithms falling into local optimality. The sparrow search algorithm (SSA) has significant optimization performance, but still has the problem of large randomness and is easy to fall into the local optimum. For this reason, this paper proposes a learning sparrow search algorithm, which introduces the lens reverse learning strategy in the discoverer stage. The random reverse learning strategy increases the diversity of the population and makes the search method more flexible. In the follower stage, an improved sine and cosine guidance mechanism is introduced to make the search method of the discoverer more detailed. Finally, a differential-based local search is proposed. The strategy is used to update the optimal solution obtained each time to prevent the omission of high-quality solutions in the search process. LSSA is compared with CSSA, ISSA, SSA, BSO, GWO, and PSO in 12 benchmark functions to verify the feasibility of the algorithm. Furthermore, to further verify the effectiveness and practicability of the algorithm, LSSA is compared with MSSCS, CSsin, and FA-CL in CEC 2017 test function. The simulation results show that LSSA has good universality. Finally, the practicability of LSSA is verified by robot path planning, and LSSA has good stability and safety in path planning.

scholarly journals VIBRANT SEARCH MECHANISM FOR NUMERICAL OPTIMIZATION FUNCTIONS

2018 ◽  
Author(s):  
Moh’d Khaled Yousef Shambour
Keyword(s):  
Convergence Rates ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Search Performance ◽  
Search Spaces ◽  
Search Mechanism ◽  
Heuristic Technique

Recently, various variants of evolutionary algorithms have been offered to optimize the exploration and exploitation abilities of the search mechanism. Some of these variants still suffer from slow convergence rates around the optimal solution. In this paper, a novel heuristic technique is introduced to enhance the search capabilities of an algorithm, focusing on certain search spaces during evolution process. Then, employing a heuristic search mechanism that scans an entire space before determining the desired segment of that search space. The proposed method randomly updates the desired segment by monitoring the algorithm search performance levels on different search space divisions. The effectiveness of the proposed technique is assessed through harmony search algorithm (HSA). The performance of this mechanism is examined with several types of benchmark optimization functions, and the results are compared with those of the classic version and two variants of HSA. The experimental results demonstrate that the proposed technique achieves the lowest values (best results) in 80% of the non-shifted functions, whereas only 33.3% of total experimental cases are achieved within the shifted functions in a total of 30 problem dimensions. In 100 problem dimensions, 100% and 25% of the best results are reported for non-shifted and shifted functions, respectively. The results reveal that the proposed technique is able to orient the search mechanism toward desired segments of search space, which therefore significantly improves the overall search performance of HSA, especially for non-shifted optimization functions.   

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