Metaheuristic Anopheles Search Algorithm: An Natural Inspired Phenomena Utilized in Engineering Optimization Problems

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1. Introduction

Nowadays, different optimization problems have been solved using various optimization techniques mainly try to obtain optimal solution in the proximity of initial point [1]. Due to increase in complexity of optimization problems and number of optimal points, efficiency of finding global optima has decreased [2]. Thus the new natural phenomena inspired algorithm known as metaheuristic anopheles search algorithm might be utilized in engineering optimization problems with continuous design variables [3]. The algorithm explores search space using random search in order to eliminate unnecessary information and this algorithm might be utilized in engineering optimization problems with continuous design variables.

 2. An review about Metaheuristic Algorithms

In computer science and optimization a metaheuristic is a higher level  procedure  or  heuristic designed to obtain, generate, or select a heuristic  that may provide a sufficiently best solution to an optimization problem, especially with imperfect information or limited computation capacity [4]. Metaheuristics used for

  • Combinatorial Optimization problems – a general class of IP problems with discrete decision variables and finite solution space. Objective function and constraints could be non-linear too. Uses relaxation techniques to the search space.
  • Constraint Programming problems – used for timetabling and scheduling problems. Uses constraint propagation techniques that reduce the variable domain. Declaration of variables is a lot more compact
1: Framework of Metaheuristic Search Algorithm

3. Inspiration of Metaheuristic Anopheles Search Algorithm

Anopheles search algorithm is inspired by behavior of the Anopheles mosquito in transmitting Malaria disease. Anopheles is a genus of mosquito species and it takes a major role in the transmission of the most dangerous malaria parasite species to humans. Malaria is transmitted from one human to another human by the female anopheles mosquito, one of the most capable vectors of human disease. The malaria parasite life cycle involves two hosts. During a blood meal, a malaria infected female Anopheles mosquito inoculates sporozoites into the human host. Sporozoites infect liver cells and release merozoites. In the bloodstream the merozoites develop into gametocytes and passed to mosquito feeding on host and gametocytes produce sporozoites which are passed to another human host and then the cycle continues.

Fig 2: Inspiration of Metaheuristic Anopheles Search Algorithm

5. Metaheuristic Anopheles Search Algorithm 

Step 1: Initialize the optimization problem is defined as follows:

Where f (T) is the objective function, T is the set of decision variables,  is the set of possible values for each decision variable and N is the number of decision variables. If decision variables are continuous, they are defined using upper and lower bounds as defines as below;

Step2: Evaluate initial fitness of each Anopheles and select best fitness

In this step, mosquitos are randomly distributed in the search space and optimality of the point where they are located is calculated.

Step 3: Calculate Distance from Best Anopheles

Randomly moving in the solution space, mosquitoes determine optimality of each point. After a few rounds, more mosquitoes will be attracted to optimal points. The odor density in natural space can be simply calculated as follows;

Step4 : Update position of each Anopheles

According to Eq. (4), odor density sensed by each mosquito and it moves towards the optimal point based on the achieved value is derived ad below;

Step 5: Evaluate fitness of each Anopheles and update best fitness

The number of iterations without any change in optimal function is appropriate for problems with solution convergence. The Stopping condition might be one of the followings;

Achieving a good enough solution is a proper one for problems with determined optimality limit.

6. Similarities between Covid -19 and Malaria

The recent spread of corona virus disease 2019 (COVID-19) constitutes an important and unsolved public health problem with potentially serious economic and social consequences [5]. Scholars and the World Health Organization (WHO) are analyzed the similarities between COVID-19 and malaria. From the analysis of distribution data, it is clear to understand the symptoms of two diseases are almost equal [6]. The drugs normally used for malaria have been promoted and used for corona also (e.g., chloroquine, amodiaquine, mefloquine, or doxycycline).


Fig 3: Symptoms of Covid-19 & Malaria
Fig 4: Preventation measures of Covid -19 & Malaria

7. Pseudo Code of Metaheuristic Anopheles Search Algorithm

Fig 5: Pseudo Code of Metaheuristic Anopheles Search Algorithm

8. Advantages & Disadvantages of Metaheuristic Anopheles Search Algorithm


Fig 6: Advantages & Disadvantages

9. Applications of Metaheuristic Anopheles Search Algorithm

The efficiency of Anopheles optimization algorithm is evaluated using various applications. They are as follows;

  • Travelling Salesman Problem [7].
  • Engineering Design problem [8].
  • Dynamic Programming [9].
  • Non- Linear Programming [10].
  • Linear Programming [11].
Fig 7: Applications of Metaheuristic Anopheles Search Algorithm

Reference:

1. Baloochian H, Ghaffary H, Balochian S (2020) Metaheuristic anopheles search algorithm. Evolutionary Intelligence. doi: 10.1007/s12065-019-00348-w

2. Kumar P, Kumar A, Pandey A (2016) Spectral Characterization of Himalayan Near-Fault Ground Motion. Periodica Polytechnica Civil Engineering 60:205-215. doi: 10.3311/ppci.7754

3. Ali M, Khompatraporn C, Zabinsky Z (2005) A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems. Journal of Global Optimization 31:635-672. doi: 10.1007/s10898-004-9972-2

4. Bakhshipour M, Jabbari Ghadi M, Namdari F (2017) Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing 57:708-726. doi: 10.1016/j.asoc.2017.02.028

5. Khandelwal A, Bhargava A, Sharma A (2019) Voltage stability constrained transmission network expansion planning using fast convergent grey wolf optimization algorithm. Evolutionary Intelligence. doi: 10.1007/s12065-019-00200-1

6. Khandelwal A, Bhargava A, Sharma A (2019) Voltage stability constrained transmission network expansion planning using fast convergent grey wolf optimization algorithm. Evolutionary Intelligence. doi: 10.1007/s12065-019-00200-1

7. Tsai C, Huang K, Yang C, Chiang M (2014) A fast particle swarm optimization for clustering. Soft Computing 19:321-338. doi: 10.1007/s00500-014-1255-3

8. Civicioglu P, Besdok E (2011) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review 39:315-346. doi: 10.1007/s10462-011-9276-0

9. Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A (2019) A survey on new generation metaheuristic algorithms. Computers & Industrial Engineering 137:106040. doi: 10.1016/j.cie.2019.106040

10. Frajberg D, Fraternali P, Torres R et al. (2019) A Testing Framework for Multi-SensorMobile Applications. Journal of Mobile Multimedia 15:1-28. doi: 10.13052/jmm1550-4646.15121

11. Frajberg D, Fraternali P, Torres R et al. (2019) A Testing Framework for Multi-SensorMobile Applications. Journal of Mobile Multimedia 15:1-28. doi: 10.13052/jmm1550-4646.15121

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