Find-Fix-Finish-Exploit-Analyze (F3EA) metaheuristic algorithm

Tuesday 20, October, 2015

Find step Fix step Finish step Exploit step Analyze step

                     

Targeting systematically analyzes and prioritizes targets and matches appropriate lethal and nonlethal actions to those targets to create specific desired effects that achieve the objectives. The emphasis of targeting is on identifying resources (targets) the enemy can least afford to lose or that provide him with the greatest advantage, then further identifying the subset of those targets which must be acquired and engaged to achieve friendly success.

General Stanley A. McChrystal wrote in 2014 about a targeting cycle called "F3EA" and used in the Afghanistan and Iraq wars, which stands for:

  1. Find: describes the process through which targets and their elements (person or location) are identified.

  2. Fix: is the process which allows prepared targets to be monitored in preparation for actioning. This phase includes the generation of a precise location of target in the battlespace and the maintenance of a track on the target. This phase also includes the continual update of situational awareness of the local area and detection of indicators and warnings of changes to the situation.

  3. Finish: is the term describing the process by which targets are actioned. A raiding force is assigned to capture, kill or destroy the target.

  4. Exploit: describes the process by which opportunities presented by the target effect are identified. Once secured, the target site must be exploited. This phase includes sensitive site, gathering information from the target area, battle damage assessment, and information actions to exploit the outcome of the finish phase.

  5. Analyze: This phase is an assessment phase that pertains to the results of attacks on targets used to restart the F3EA cycle to identify further targeting opportunities

The F3EA metaheuristic algorithm specifically mimics the targeting process of selecting objects or installations to be attacked, taken, or destroyed in warfare. Although the metaphor of F3EA metaheuristic algorithm comes from targeting process in welfare as described above, it uses the underlying theories of Physics of radar and projectile motion in the so called Find and Finish steps, respectively. The F3EA metaheuristic algorithm is a population based algorithm in which each individual in the population is related to one of enemy’s artificial facilities and its position on the function surface (in Rn+1) is related to the position of the relevant facility on the battle ground. The mechanism of F3EA algorithm is composed of five steps:

The Find step imitates the process of selecting objects or installations to be attacked, taken, or destroyed in warfare. In this step, which is essentially a selection step, a new selection mechanism is introduced. Here we imitate the detection process followed by military radar devices to analyze and prioritizes targets. More specifically we adopt the radar range equation from Physics and develop our theory to decide whether an individual is detectable to bias the search toward it or not. The emphasis of Find step is on identifying individual solutions that provide a great advantage if their surrounding solutions being searched. To generate a new solution from an individual parent solution in the population, we assume that the parent solution temporarily makes role as a radar device and all other individuals are assumed to be artificial military facilities which may or may not be detected by this artificial radar for destruction (see Figure 1). In this way, in the proposed selection mechanism, only an individual which is detectable by the artificial radar can be selected by which a new solution being generated during the Finish step. 

 

Figure 1. Military objects with different sizes and different distances from the radar may or may not be detected.

 

The Fix step which is a local search step mimics the aiming process toward the target to monitor it in preparation for actioning. Figure 2 demonstrates a schematic concept of the Fix step. As can be seen from this figure, to hit the military tank target, the angle by which the rocket is launched should be acute enough that the rocket traverses the highest peak. This means that the aiming should be in such a way that it considers the highest peak position on the rocket trajectory. Both of the blue and black trajectories in Figure 2, yield an unsuccessful launch. Any successful launching trajectory must pass over the highest peak. We implement such an idea as a continuum of single-variable optimization problems to scan the function surface on a given path to obtained locally improved solutions. In this way, the Fix step of F3EA metaheuristic can be thought as a local search phase, in general.

 

Figure 2. A schematic concept of the Fix step in F3EA metaheuristic

 

The Finish step develops an adaptive mutation stage by which new solutions are generated within the search space. In this step it is assumed that the detectable individuals selected in the Find step are enemy’s facilities for which appropriate lethal actions should be taken to destruct them. A number of artificial rockets are launched from the base individual positions in the search space toward such enemy’s objects and their explosion place can be thought as of the position of the new solutions within the search space (see Figure 3). Here we use the theory of projectile motion of Physics to develop a mathematical model to generate a new solution in the search space.

 

Figure 3. A schematic concept of the Finish step in F3EA metaheuristic

 

In the Exploit step, we seek for opportunities presented in mating the new solution, generated in the Finish step, and those individuals already exist in the population. The last step, the Analyze step, is a replacement step by which whenever a new solution generated during Fix or Exploit step provided better function value, it enters into the population or updates the global best solution found so far. Figure 4 demonstrates the infograph of F3EA metaheuristic algorithm. 

 

Figure 4. The infograph of the F3EA metaheuristic algorithm

 

To find more details on the whole mechanizm of F3EA metaheuristic algorithm and its mthematics please see the following materials. 

 
To download an introductory draft paper on F3EA metaheuristic algorithm (in English)
       
To download an introductory draft paper on F3EA metaheuristic algorithm (in Persian)
       
To download the Powerpoint slides of F3EA metaheuristic algorithm (in English)
       
To download the Powerpoint slides of F3EA (short version in Persian)
       
To download the Matlab source code of F3EA for unconstrained optimization 
       

To download an oral presentation of F3EA metaheuristic in FLV format (in Persian)
 

     Researches on F3EA metaheuristic


  1. 1.Husseinzadeh Kashan A, Tavakkoli-Moghaddam R, Gen M (2016). A warfare inspired optimization algorithm: the Find-Fix-Finish-Exploit-Analyze (F3EA) metaheuristic. Tenth International Conference on Management Science and Engineering Management, ICMSEM 2016.

  2. Husseinzade Kashan A (2015). A New Metaheuristic Inspired by Find-Finish process in Battlefields. International Conference on Industrial and Systems Engineering (ICISE 2015), 669-676.

  3. Husseinzade Kashan A (2014). Find-Fix-Finish-Exploit-Analyze (F3EA) metaheuristic: new selection, variational and local search operators inspired by targeting process in contemporary warfare. Technical paper, Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran.